Earlier this week, Cyera announced an intent to acquire Oasis Security. To understand the nature of this acquisition, it should be viewed as a strategic initiative designed to merge data security, non-human identity (NHI), and agentic security into a unified control-plane narrative.

Executive Summary

• SACR’s assessment is that the acquisition is strategically coherent because of the rise of AI agents, but work lies ahead to make it successful. This acquisition gives Cyera a credible path into the identity market and agentic security, but the value of the deal will depend on whether the company can unify data and identity context at the policy and enforcement layers.

• It is well-known news that Data has become a catalyst for agentic security transformation. Similarly, agentic identities are believed to now surpass humans 144:1 in cloud environments and are believed to be outnumbering human users by an average of 45 to 1.¹

• Data and identity security have historically been managed by separate enterprise teams with different budgets, systems of record, workflows, and policy authorities. However, AI agents may become the catalyst that forces these domains closer together. Agents routinely operate through delegated human permissions, service accounts, APIs, workload identities, and automation frameworks while accessing and acting upon sensitive enterprise data. As a result, organizations can no longer evaluate identity risk independently from the sensitivity, purpose, and context of the data being accessed.

• This creates an opportunity for Cyera and Oasis to bridge the historical divide between data-security and identity-security teams by providing a shared understanding of what data exists, which human or non-human identities can access it, what actions they are taking, and whether those actions should be permitted. Yet AI-driven convergence at the technology layer will not automatically resolve fragmented ownership at the organizational layer. Cyera must still prove that the combined platform can establish a clear economic buyer, enable workable cross-functional governance, and deliver credible runtime enforcement.

• The market should therefore evaluate the acquisition through integration depth, customer adoption, enforcement authority, organizational alignment, and measurable security outcomes. AI agents may accelerate the need for data and identity convergence, but Cyera must demonstrate that it can turn that market pressure into an operationally unified and commercially viable control plane.

• Disclosure: Cyera is a SACR advisory client. SACR’s analysis remains independent, and vendors do not receive editorial control.

The Organizational Reality of Data & Identity Teams

Data security and identity security have historically developed as separate enterprise disciplines.

Data-security teams typically concentrate on:

• Data discovery and classification

• DSPM

• DLP

• Privacy and regulatory requirements

• Data lineage and residency

• Data exposure and exfiltration

• Collaboration with data owners and privacy teams

Identity teams typically concentrate on:

• Identity lifecycle management

• Authentication and federation

• IGA

• PAM

• Entitlements and access reviews

• Segregation of duties

• Service accounts and workload identities

• Integration with HR, directories, cloud IAM, and application owners

These teams often use different systems of record, operate under different leaders, and have different definitions of risk. The argument is that AI agent security has converged these two together.

Cyera’s acquisition thesis attempts to connect these areas. However, the enterprise does not necessarily have an established function responsible for the combined answer. The collapse of the technical boundary between data and identity does not automatically collapse the organizational boundary between the teams responsible for them.

Cyera may therefore have to create not only a new product architecture, but also a new enterprise operating model involving:

• Shared policy ownership

• Joint risk scoring

• Escalation procedures

• Cross-functional remediation

• Data-owner participation

• Identity-team approval

• Security operations involvement

• AI governance oversight

Strategic Rationale: Data, Identity, and Agentic Security Converge

Cyera has been moving from a DSPM leader toward a broader trusted layer for AI: DSPM, DLP, identity-aware access governance, and agentic security. Cyera maintains a solid data-security framework that includes Data Security Posture Management (DSPM) and Data Loss Prevention (DLP), AI Guardian, and data-context governance. By incorporating Oasis, Cyera gains specialized expertise in NHI, particularly in the posture (hygiene) and protection of service accounts, secrets, API keys, workload identities, and developing machine/agent identities.

Oasis gives Cyera a highly relevant entry point into this broader vision. 78% of organizations don’t have documented and formally adopted policies for creating or removing AI identities.² Agentic AI will intensify the threat of non-human identities, because agents frequently operate through delegated credentials, service accounts, APIs, MCP servers, and automation frameworks. 79% of IT Pros Feel Ill-Equipped to Prevent Attacks Via Non-Human Identities,³ which means there’s room for growth and adoption to deal with these critical agentic identity and AI agent issues.

The acquisition therefore links three control planes that are usually fragmented but require unity for the latest perimeter of protection with agents, including data context and identity visibility and control:

The strategic idea is not simply DSPM plus NHI. It is the arrival of Cyera as a platform player where data context will shape identity decisions, and identity context should shape data-access enforcement.

Oasis As A Logical Acquisition Target

Oasis is a focused NHI security specialist with relevance across service accounts, secrets, API keys, workload identities, and identity threat detection/response and posture. SACR workspace notes indicate that Cyera evaluated alternatives and selected Oasis after due diligence, with reported customer preference for Oasis over other alternatives because of perceived integration value.

That customer pull is important. The strongest strategic rationale is not that Cyera bought an adjacent category label, but rather that customers appear to want a combined view of sensitive data exposure and machine identity access. If Cyera customers are willing to replace standalone NHI tooling because the data integration creates higher value, the deal can improve long-term platform value.

Oasis also strengthens Cyera’s ability to talk credibly about agentic security; it had made prior visibility and control moves in data security, but entry of competitive identity offers would have created longer-term gaps in the Cyera portfolio and platform. In agentic environments, the boundary between identity, data, and action collapses. This is because agents are often inheriting human permissions, use service accounts, invoke tools, call APIs, or retrieve sensitive data through MCP-style integrations. That creates a need for identity governance that is both intent-aware and data-aware to connect data access and leak prevention with the identities that use and access them.

Strategic Strengths

1. Clearer market differentiation: Cyera moves beyond DSPM alone toward a broader data and identity security platform. NHI gives Cyera a credible wedge into the larger identity security market without starting with a frontal assault on human IAM incumbents.

2. More complete security context: Combining data classification with NHI visibility can create a richer view of who or what has access to sensitive data, whether those permissions are excessive, and what actions should be allowed.

3. Agentic security alignment: Oasis strengthens Cyera’s ability to address AI agents and machine identities, especially where agents operate through service accounts, delegated credentials, APIs, and human permissions.

4. Customer-driven rationale: Cyera claims to have selected Oasis after due diligence and that customers preferred Oasis over alternatives because of the value of the integration.

5. Platform expansion: The acquisition supports Cyera’s longer-term vision of becoming a control plane across data, identity, AI, and agentic activity.

6. Behavior analytics potential: Oasis’s NHI behavioral baselining plus Cyera’s data-access baselining could enable higher-fidelity anomaly detection, especially around machine identities accessing sensitive data in unusual ways.

7. Category timing: SACR’s broader research has been pointing toward convergence among identity, NHI, AI agents, MCP, runtime enforcement, and data governance and alternative platforms. This deal lands directly in that convergence zone.

Potential Strategic Acquisition Challenges

1. Integration risk: The value depends on whether Cyera can deeply integrate Oasis into its data-centric workflow rather than simply attaching NHI features to the platform. Oasis is a focused NHI specialist; Cyera needs the acquisition to become part of a unified data-identity-action graph.

2. Go-to-market complexity: DSPM, DLP, identity, NHI, and AI security can involve different buyers, budgets, and operating teams. Cyera will need a clear sales motion that explains who buys, what gets replaced, and what gets expanded.

3. Pricing scrutiny: A high acquisition price for a focused NHI player may draw questions unless Cyera can demonstrate integration-driven value and customer pull.

4. Category clarity: Cyera must explain whether this is a DSPM expansion, an identity move, an agentic security play, or all three. The answer can be “all three,” but the messaging must be simple enough for buyers.

5. Proof burden: Cyera’s trust layer for AI and agentic-security positioning will require concrete evidence: GA vs. roadmap clarity, architecture diagrams, enforcement points, customer outcomes, and credible demos.

6. Human identity boundary: Cyera’s best path is likely through NHI and agentic identities, but agents often borrow or operate under human permissions. That creates a head-to-head competition and overlap with Okta, SailPoint, CyberArk, Palo Alto, CrowdStrike, and other identity/security platforms.

7. DLP and runtime maturity: Cyera’s Omni DLP and AI Guardian narratives are strategically compelling, but buyers will ask where enforcement happens, which channels are covered, and what is native versus new; customers will focus on integrated value.

How Cyera & Oasis Makes This Acquisition Work

GTM support since there is no natural enterprise owner for the combined platform

One of the biggest risks faced is that both teams are separated. The combined proposition spans data security, identity security, cloud security, and AI governance security. These functions commonly operate under different leaders, budgets, procurement cycles, systems of record, and success metrics.

A product that addresses several teams may appear strategically comprehensive while becoming commercially difficult to purchase because no single team owns the complete problem.

Cyera will therefore need to establish:

• Who owns the budget

• Who administers the platform

• Who approves policy changes

• Who is responsible for remediation

• Which existing platform is displaced

• Which team is accountable when identity and data policies conflict

Without clear answers, the platform could become an additional visibility layer rather than a consolidated control plane.

Integrate Cyera and Oasis into a cohesive product architecture

Integrating two security products requires more than correlating telemetry. Cyera will need to reconcile potentially different:

• Asset and identity models

• Risk-scoring methodologies

• Policy engines

• Data architectures

• Connector frameworks

• Remediation workflows

• Authorization models

• Product release cycles

If the two products continue operating as separate engines beneath a shared interface, customers may experience duplicated configuration, inconsistent risk findings, and fragmented remediation. Hence, Cyera will need to strongly integrate the Oasis product into a cohesive suite and UI experience for the users.

Integration Vision: Data-Identity-Action Graph

The bigger opportunity for Cyera is building a data-identity-action graph and strong intelligence layer to deliver risk signals across the agentic actions of users and autonomous harnesses. What’s crucial for buyers is a unified view of what data exists, which identities can access it, and what those identities are doing.

In that model, Cyera could move from passive visibility toward real-time decisions, including:

• Dynamically adjusting permissions when sensitive data is involved

• Limiting agent actions based on intent, role, and data sensitivity

• Detecting abnormal service-account or workload-identity behavior

• Quarantining or disabling risky agents

• Enforcing tighter controls when NHI activity touches regulated or high-value data

• Mapping data lineage across human, AI, and agentic workflows

• Providing audit-ready evidence of who or what accessed sensitive data and why

This also connects to Cyera’s maturity-model work. SACR notes from the Cyera maturity-model discussion suggest a forward-looking thesis: agentics may make level-five data security maturity more achievable by reducing the human/process burden of continuous discovery, classification, remediation, and enforcement. Oasis could strengthen that story by adding the identity substrate needed for automated decisions.

Cyera and Oasis: Integration Outlook

Competitive Implications

The acquisition increases Cyera’s strategic surface area. It now competes or overlaps with several categories at once:

• DSPM / data security vendors: Cyera can argue that sensitive-data context is more valuable when paired with identity and NHI behavior.

• Standalone NHI vendors: Oasis gains a broader platform and data context that NHI-only vendors may lack.

• Identity incumbents: Cyera gains a wedge into identity through NHI and agentic access rather than starting with traditional IAM.

• CNAPP / cloud platforms: Wiz, Palo Alto, CrowdStrike, and others may have cloud and identity context, but Cyera can differentiate on data precision and data-first governance.

• DLP / SSE vendors: Cyera’s Omni DLP story becomes more compelling if identity and data context can drive enforcement across channels.

• AI security / AI-SPM vendors: Cyera can position AI security as a data and identity governance problem rather than only model, prompt, or posture management.

SACR’s broader identity and agentic-security research has repeatedly emphasized that identity security is shifting from managing human logins to governing autonomous and non-human actors operating at machine speed. This deal gives Cyera a credible way to participate in that shift.

What CISOs Should Watch For

1. Integration roadmap: How quickly Oasis capabilities become native to Cyera workflows, dashboards, policies, risk scoring, and remediation.

2. GA vs. roadmap clarity: Which vendor capabilities are shipping now versus remaining in preview, Black Hat or conference demos, or longer-horizon strategy and vision.

3. Enforcement model: Where Cyera can actually enforce policy: API, proxy/gateway, endpoint, SaaS, IdP, SSE, DLP partner, or native control.

4. Customer proof: Whether customers replace standalone NHI tools, expand Cyera budgets, or adopt combined data + NHI use cases.

5. Buyer alignment: Whether Cyera can sell this to data security, identity, cloud, and AI security leaders without creating confusion.

6. Agentic identity positioning: Whether Cyera can define a crisp boundary between NHI, AI agents, human-delegated permissions, and traditional IAM.

7. Competitive response: How Palo Alto, Wiz, CrowdStrike, SailPoint, CyberArk, Okta, and standalone NHI vendors respond.

Bottom Line: SACR Perspectives

Cyera’s acquisition of Oasis is strategically coherent but structurally difficult. The deal connects two areas that increasingly influence one another: sensitive-data security and non-human identity governance. However, technical convergence does not eliminate the distinct teams, budgets, platforms, workflows, and authorities that have historically separated data and identity security.

If executed well, the acquisition positions Cyera as more than a data security platform, filling in some perceived gaps against key competitors. The merged company could become a central governance layer for sensitive data access, non-human identity risk, and agentic security.

The main caveat is execution, where Cyera must prove that this is a deeply integrated platform move, not just a category-expansion headline. This is one of the clearest examples of data security and identity security converging around agentic AI. Cyera has the right strategic ingredients, but the market will judge the deal on integration depth, enforcement proof, and buyer clarity.

SACR therefore views the acquisition as strategically promising but operationally unproven. The market should evaluate it based on integration depth, customer adoption, enforcement authority, organizational fit, and measurable security outcomes, not the breadth of the post-acquisition platform narrative alone.

Sources:

1. Cyera acquired Oasis

2. Cyera & Oasis Blog Websites

3. Cloud Security Alliance: The Non-Human Identity Governance Vacuum

4. WEF study from 2025: over half of enterprises lack defined responsibility for governing machine-based AI credentials

5. Cloud Security Alliance Official Press Release

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Part 1 established that a Trusted SOC is not purchased as a platform or achieved through autonomous actions by AI agents. Security teams/leaders build it by granting AI agents authority for specific response actions only when the evidence, governance, reliability, and verified outcomes support that authority.

This Part 2 applies that model to the market itself. We evaluated 18 vendors across three operating environments and testing scenarios.

1. The regulated SIEM-centric enterprise SOC

2. Hybrid mid-market SOC

3. Cloud native data-lake SOC

The result shows which products are positioned to support which authority portfolios, and where.

Caveats:

^{The AI SOC category is broad and still consolidating. Peer trackers count close to 140 vendors that use, market, or support AI within security operations, and SACR treats more than 60 of those as pure-play AI SOC platforms worth direct comparison. This report evaluates 18 of them in depth. This report is purely independent, with no vendor relationship influencing any rankings. Everything was weighted}

^{That narrower set was not a matter of convenience. To qualify for ranking, a product had to materially shape the path from case understanding to response, through decisioning, action recommendation, governed execution, or verification, backed by enough generally available capability and production evidence to support a defensible comparison rather than a restated pitch deck. Inclusion in the ranked 18 is a statement about evaluability, not a statement about quality. A vendor’s absence from the ranked set is not a judgment against it. It typically means the evidence available to us during the research period did not yet support a comparison at the same depth as the vendors we ranked.}

^{This analysis builds on the same primary research described in Part 1: structured interviews with [20] security leaders and practitioners, and briefings or live demonstrations across the broader vendor landscape.}

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Key Actionable Summary

The first report identified the action gap between incident understanding and trusted response. Our market analysis finds that this gap remains the dividing line between broad capability and category leadership. Investigation, summarization, and enrichment have become common across the market. The systems that carry evidence into decisioning, governed action, execution, verification, and proof remain far less consistent. Products that appear similar in a demonstration can create substantially different operating models in production.

• Evaluation model: We evaluated 18 vendors across Architectural Alignment and Production Delivery. Architectural Alignment measures how completely a product connects the lifecycle from evidence through verified outcome. Production Delivery measures how reliably it provides that capability in real operating environments today.

• Market finding: Leadership is architecture-neutral. AI-native, SOAR-derived, and platform-consolidated products can all support the Trusted SOC. Their strengths differ according to lifecycle ownership, governance, production maturity, integration requirements, and operational burden.

• Top-line results: No single vendor can be considered a universal AI SOC leader. Suitability materially changes based on evidence architecture, control-plane ownership, governance intensity, operating capacity, and the response actions organizations intend to delegate in their own environments.

• Environmental finding: Vendor fit changes across the regulated SIEM-centric enterprise SOC, hybrid mid-market SOC, and cloud native data-lake SOC. Each environment places different demands on governance, integration, data access, execution, and operational overhead.

• Buyer takeaway: Use the Trusted SOC as the destination and these rankings as a guide to the most credible path for your environment. Evaluate how much of the lifecycle the product can own today, what authority it can safely exercise, how it proves the outcome, and what your organization must supply around it.

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What Buyers Are Purchasing

AI SOC purchases today take one of three primary forms. Each places control of evidence, case state, decisioning, and response in a different part of the security stack. The practical choice is where the buyer wants this operating layer to reside, which systems should remain authoritative, and how much integration, maintenance, and governance burden the organization is prepared to own.

Path 1: Add an AI-Native Layer Over the Existing Stack

Organizations choosing an independent AI-native platform generally want to preserve their current SIEM, EDR, identity, cloud, and workflow tools while adding a system that can assemble evidence, investigate incidents, develop cases, and coordinate response across them. This path offers cross-stack flexibility, modern reasoning, and faster deployment without requiring broad platform consolidation. Its effectiveness depends on integration depth, API access, credential design, customer context, and the platform’s ability to verify actions executed through external control systems.

This path is a natural fit for hybrid mid-market and cloud native teams that operate fragmented environments, need greater analyst leverage, or want to improve investigation and response without replacing the surrounding stack. Buyers assume responsibility for deciding which systems remain authoritative and how the AI-native layer receives enough context and permission to act safely.

Path 2: Modernize SOAR and Automation with AI

Organizations with established orchestration, case management, and response workflows may extend those systems with AI-assisted investigation and decisioning. This path builds on existing strengths in connector breadth, approvals, deterministic execution, auditability, retry logic, and failure handling. AI can improve prioritization, evidence gathering, case development, playbook selection, and the handling of incidents that require interpretation rather than a fixed sequence of steps.

This path is well suited to regulated enterprises and operationally mature SOCs that already rely on formal workflows and controlled execution. Buyers gain a familiar governance foundation and may face greater workflow administration, integration maintenance, and process complexity. They should determine how deeply AI reasoning is connected to the execution layer and whether the system can preserve evidence continuity from investigation through verified closure.

Path 3: Consolidate Into a Broader Security Platform

Organizations already standardized on a major security platform may extend that platform across telemetry, detection, investigation, case management, and response. Native access to endpoint, network, identity, cloud, and threat-intelligence data can reduce integration friction and connect decisions directly to enforcement. This path can provide strong production scale, established governance, and a more unified operating experience across the platform’s installed ecosystem.

This path is most natural for large or regulated enterprises that prioritize consolidation, native control, and operational consistency. Buyers should evaluate the breadth of third-party support, the amount of additional platform adoption required, and the degree to which the system can maintain case and evidence continuity outside its native ecosystem.

Choosing the Operating Model

Each path can support progress toward a Trusted SOC. The purchasing decision should begin with the authority portfolio the organization wants the product to assume: which evidence it can access, which investigations it can conduct, which actions it can recommend or execute, where approval is required, which credentials it can use, and how outcomes will be independently verified.

Sourcing and delivery choices cut across all three paths. Organizations may operate the product directly, deploy it with vendor assistance, use it through a co-managed or managed service, or combine vendor capabilities with internally built agents and automation. These choices shift the staffing, maintenance, accountability, and proof burden between the customer, the software provider, and the service operator.

This makes environment-specific evaluation essential. The buyer is selecting a product to assume defined responsibilities inside a particular security operating model, with evidence that it can perform those responsibilities safely, reliably, and at the maturity required by the organization.

Research Methodology

We developed this report through primary and secondary research conducted across the AI SOC market. We collected evidence through security-practitioner interviews, vendor briefings, live product demonstrations, targeted surveys, product documentation, and supporting market research. Practitioner research established the operating problems, architectural constraints, adoption requirements, and purchasing considerations shaping real deployments. Vendor research provided evidence on product architecture, integrations, investigation workflow, decision systems, response capability, governance, deployment maturity, and customer use.

We assessed capabilities according to what vendors could demonstrate or support with available evidence. Greater weight was given to generally available product functionality, live workflow, documented integrations, production use, and customer evidence. Beta, private-preview, and roadmap capabilities were separated from mature functionality and did not receive equivalent credit. Product positioning and general statements about automation were not treated as evidence of response authority or production maturity.

The market is developing quickly, and the available evidence remains uneven. Vendor participation, access to live deployments, customer references, and measurable production outcomes varied across the evaluated companies. The findings represent SACR’s assessment of the best evidence available during the research period and should be understood as a current view of a rapidly changing market.

Evaluated Vendors

The AI SOC market now includes over 100 companies that use, market, or support AI in security operations. The ranked cohort was limited to products whose relevant AI SOC offering was publicly identifiable by December 31, 2025, which materially addressed the path from case-level understanding to governed response, which fit one of the three customer-operated product paths evaluated, and had sufficient verifiable evidence of current capability and production maturity to support a defensible comparison. This report is not meant to serve as a census of every vendor in the market, it is a comparative analysis of platforms that buyers can reasonably evaluate as a path to operating a Trusted SOC.

To select the ranked cohort we began with the broader AI SOC ecosystem, including AI-native SOC platforms, SOAR and automation providers, security-data and analytics platforms, large security suites, builder tools, and service-led offerings. We then applied a scope screen to narrow the list.

To be included in the ranked set, a product had to materially shape the path from case-level understanding to response: through AI-assisted decisioning, action recommendation or preparation, approval and policy controls, execution, verification, or a demonstrable combination of those functions. Products whose primary role is telemetry collection, data management, investigation support, or managed service delivery remain relevant to the broader market, but are not automatically comparable to response-layer platforms.

We evaluated products rather than entire companies. For broad platform vendors, only the AI SOC, SOAR, or response capabilities relevant to this analysis were assessed. A vendor’s larger security portfolio, revenue, market capitalization, or general brand presence did not determine inclusion or placement.

The evaluated set also required enough evidence to support a defensible comparison. SACR considered live workflows, product documentation, integration depth, generally available functionality, deployment maturity, customer evidence, and the distinction between production capabilities and roadmap claims.

Finally, the 18 vendors were selected to represent the principal architectural and operating choices available to buyers: AI-native SOC platforms, SOAR-derived platforms with AI, and broader security providers with identifiable AI SOC capabilities. The set is broad enough to compare those approaches across regulated SIEM-centric enterprise, hybrid mid-market, and cloud native data-lake environments without treating every adjacent product as a like-for-like competitor.

Inclusion in this report does not mean that these are the only relevant vendors in the market, neither does absence constitute a judgment that another product lacks value. It means we had a sufficient basis to evaluate these products against the specific case-to-response criteria and operating environments used in this analysis.

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AI SOC Market Rankings

Cross-Environment Capability View

The comparative average consolidates performance across three operating environments. Vendor placement reflects two separate questions. Architectural Alignment measures how completely the product connects the AI SOC lifecycle from evidence through verified outcome. Production Delivery measures how reliably the product delivers those outcomes in real-world environments today. Together, the axes distinguish architectural completeness from demonstrated operational maturity.

Architectural Alignment does not reward a particular product heritage. AI-native, SOAR-derived, and platform-consolidated products can all score highly when they connect the complete lifecycle as a coherent system. Production Delivery is based on generally available capability, operational evidence, governance, scale, failure handling, and verified customer use.

Architectural Alignment

Architectural Alignment measures how completely the product’s core architecture supports the AI SOC lifecycle:

• Evidence assembly

• Investigation

• Decisioning

• Bounded authority

• Response execution

• Outcome verification

• Continuous improvement

A high score means these capabilities operate as one connected evidence-to-outcome system. A lower score means ownership is distributed across the product and adjacent systems, requiring another platform, automation layer, investigation tool, or human team to complete one or more stages of the lifecycle.

Architectural Alignment measures the connectedness and ownership of the lifecycle. It is not a measure of overall product quality, company maturity, or architectural heritage.

Production Delivery

Production Delivery measures whether the product can reliably deliver AI SOC outcomes in real-world environments today. It considers:

• Production maturity

• Integration depth

• Deployment burden

• Governed execution

• Operational scale

• Failure handling

• Case continuity

• Evidence that response actions achieve the intended result.

A high score means the product can move supported incidents from investigation through controlled action and verified closure, with credible customer evidence. A lower score indicates narrower execution coverage, limited production proof, greater operational burden, or dependence on another system or human team to complete the workflow.

Innovators Category

Innovators combine strong Architectural Alignment with credible Production Delivery. They connect evidence, investigation, decisioning, bounded authority, response execution, and outcome verification within a continuous workflow, with sufficient production evidence to demonstrate that the model functions in real customer environments.

Vendors reach this position through different routes. Torq and BlinkOps emphasize automation and integration fabrics that make controlled action dependable across a wide stack. 7AI and Prophet Security emphasize AI-native case progression and graduated authority. Exaforce is differentiated by its data architecture and cloud native evidence assembly. Swimlane reaches the category through a SOAR-derived architecture that combines federated evidence access, production AI reasoning, mature governance, and dependable cross-stack execution. Their shared characteristic is demonstrated strength across both Architectural Alignment and Production Delivery.

Trailblazers Category

Trailblazers demonstrate strong Production Delivery with moderate Architectural Alignment. Their strengths include established integrations, durable workflows, governance, auditability, operational scale, and credible production use. Ownership of the complete AI SOC lifecycle is more distributed across the surrounding platform or automation architecture.

Palo Alto Networks reaches this position through platform consolidation, native telemetry and enforcement, and mature enterprise response controls. For buyers whose environment align with this operating model, it may provide the strongest practical fit.

Pioneers Category

Pioneers demonstrate strong Architectural Alignment with developing Production Delivery. Their products are closely organized around a connected AI SOC lifecycle, while the production record remains younger or less complete across execution breadth, deployment duration, governance depth, verification, integrations, or enterprise scale.

Mate Security and Simbian emphasize organizational context, agentic investigation, and staged authority. AIStrike connects detection improvement with investigation and response. Radiant Security and Dropzone AI emphasize analyst leverage and rapid case development. Intezer and Qevlar provide strong evidence assembly and reasoning with narrower governed-remediation depth. Broader verified execution and stronger production evidence can move these vendors toward the Innovator category.

Emerging Players Category

Emerging Players have relevant AI SOC capabilities and strong adjacent control planes, while complete lifecycle ownership and Production Delivery remain developing. Their capabilities may be concentrated in endpoint or XDR response, investigation, workflow automation, case management, or another portion of the AI SOC lifecycle.

CrowdStrike and SentinelOne bring mature native telemetry and enforcement platforms. Stellar Cyber brings an established Open XDR and case-management foundation. D3 Security brings deep SOAR, orchestration, and governance experience. Their placement reflects the current connectedness and demonstrated delivery of their evaluated AI SOC capabilities. It does not describe the maturity, market position, or overall quality of the companies.

Environment Specific Ranks

Each environment applies the same Trusted SOC destination under different operating conditions. What changes is the evidence, governance, integration, and operational burden a product must satisfy before it can credibly support response authority.

The depicted environments are meant to serve as examples of different types of SOCs. It is not an exhaustive list that captures every nuance an environment can have. Large enterprises may have fragmented security stacks, and a cloud native organization may be subject to strenuous industry regulation. We encourage buyers to leverage our analysis as a baseline of what a vendor can offer, and apply it to their own unique operating environment and regulations.

Environment 1: Regulated SIEM-Centric SOC

A mature SOC operating in a highly regulated industry where the SIEM remains the primary system of record for detection, alert correlation, investigation workflow, compliance reporting, and escalation. The mature SOC has established processes, tiered analysts, defined incident response procedures, and strict requirements for auditability, approval, and evidence retention.

Generic architecture

• Enterprise SIEM as the central detection and correlation layer

• Mature telemetry coverage across endpoint, identity, cloud, network, email, SaaS, and threat intelligence sources

• SOAR, ITSM, ticketing, and case management workflows

• Response integrations across EDR, IAM, email security, firewall, cloud controls, PAM, and vulnerability management

• Formal governance through RBAC, policy libraries, audit logs, approval gates, and evidence retention

• 24/7 or follow-the-sun SOC coverage with clear analyst tiers and incident command structure

• High regulatory pressure from financial services, healthcare, government, critical infrastructure, or public company obligations

The regulated map rewards vendors that can prove both authority and accountability. Palo Alto Networks and Swimlane rise because mature platform or SOAR controls are highly valuable in this environment. Torq, 7AI, and BlinkOps remain strong because they combine modern reasoning with bounded execution. Vendors that are excellent at investigation but cannot yet show formal governance, durable case continuity, and verified post-action state are pushed toward the specialist or contender categories.

Enterprise Execution Axis

The horizontal axis measures whether the vendor can operate reliably as part of a large enterprise response system. It incorporates SIEM and workflow integration, breadth of response orchestration, deployment maturity, scale, failure handling, case continuity, and the practical ability to execute across a complex control stack.

A high score means the product can move beyond analysis into repeatable production action across enterprise systems. A lower score generally reflects narrower action breadth, less mature deployment evidence, a younger integration catalog, greater dependence on a particular architecture, or more limited proof that the product can carry enterprise operating load.

Governance and Decision Assurance Axis

The vertical axis measures whether the platform can make and govern consequential decisions in a way that a regulated enterprise can defend. It includes decision quality, evidence traceability, approval routing, policy controls, auditability, chain of custody, blast-radius limits, source-state verification, and outcome proof.

A high score means the platform can explain why an action was selected, constrain who or what can authorize it, preserve the decision record, and show what happened afterward. A lower score does not necessarily mean weak security efficacy; it often means the reasoning-to-action chain is fragmented, approval controls are incomplete, rollback is inconsistent, or post-action verification is not sufficiently mature.

Governed Leaders Category

This is the strongest position for a regulated enterprise buyer. Vendors in this quadrant combine substantial execution authority with the controls required to use that authority safely. They can support durable cases, policy-aware decisions, approvals, audit trails, and production response across meaningful parts of the enterprise stack.

The vendors are not identical. Some reach this quadrant through mature platform-native enforcement, some through SOAR and orchestration depth, and others through AI-native reasoning coupled to bounded action tools. Their common characteristic is that neither action breadth nor governance is an obvious disqualifier for a regulated deployment.

A regulated buyer should treat this quadrant as the primary shortlist, then separate vendors by operating model. Palo Alto Networks and CrowdStrike are strongest when platform consolidation is acceptable. Swimlane is strongest when mature SOAR governance and deployment optionality are central. Torq and BlinkOps are attractive when the buyer wants a more independent orchestration layer. 7AI is strongest when reasoning-led case progression and graduated autonomy matter more than long enterprise tenure.

Decision Assurance Specialists Category

These vendors show credible reasoning, transparency, policy control, or evidence assurance, but do not yet demonstrate the same breadth, scale, integration maturity, or production operating depth as the Governed Leaders. They are not weak products, their placement means that the buyer can more readily trust how they reach a decision than assume they can execute every enterprise action at scale.

This category contains several different product shapes. AI-native platforms such as Prophet, Simbian, and Mate score well because their control design is explicit. D3 Security reaches the quadrant through mature SOAR governance. Exaforce and Qevlar bring strong evidence models. AIStrike benefits from risk-tiered authority and detection feedback. Their common limitation is not necessarily reasoning quality; it is the maturity or breadth of enterprise execution proof.

This category is useful for enterprises that already possess a strong execution layer or are willing to keep consequential action under existing SOAR, platform, or human control. These products can materially improve decision quality and case confidence even when they are not selected as the universal remediation fabric. Buyers should test integration burden, approval handoffs, action coverage, and whether the product can preserve one continuous evidence record when execution moves to another system.

Enterprise Executors Category

Enterprise Executors can perform substantial security action and have credible deployment scale, but the full reasoning-to-case-to-action-to-proof chain is less mature than their enforcement capability. These vendors often have strong control-plane assets, installed bases, and deterministic response functions. Their lower assurance position reflects fragmentation, limited dynamic reasoning, connector-dependent verification, or incomplete evidence that the newest agentic layer operates with the same governance maturity as the underlying platform.

These vendors are credible when native platform action is more important than introducing a separate response layer. The diligence focus should be on whether the product can preserve one case record, explain why a dynamic action was selected, enforce approval and credential boundaries, and independently verify the resulting state. Buyers should not assume that mature deterministic response automatically proves mature agentic response.

Qualified Contenders Category

Qualified Contenders provide meaningful operational value but do not yet meet the regulated scenario’s highest bar on both dimensions. The placement often reflects a product whose strength is investigation, triage, or practical analyst acceleration rather than enterprise-wide governed remediation.

This category is not defined as an exclusion category. It identifies products that may be valuable in a narrower role, but which require additional controls, an external execution system, more human approval, or more evidence before they should become the primary autonomous response layer for a regulated enterprise.

Environment 2: Hybrid SOC

An organization with a mixed SIEM and data-lake architecture, a lean security team, uneven telemetry maturity, and a practical need to reduce analyst workload without rebuilding the SOC. The SIEM still exists, but it is no longer the only place where security data lives. The data lake or security data platform is used for broader telemetry, historical search, enrichment, and cost control.

Generic architecture

• SIEM for high-value alerts, mature detections, and compliance-relevant logs

• Data lake, security data platform, or warehouse for broader telemetry and lower-cost retention

• Telemetry from endpoint, identity, cloud, email, SaaS, network, vulnerability, and application sources

• Mixed workflow across ticketing, case management, Slack or Teams, SOAR-lite, and manual analyst processes

• Response integrations across EDR, IAM, cloud console, email security, firewall, SaaS admin tools, and ticketing

• Small internal SOC, hybrid SecOps/IT team, or co-managed MDR support

• Moderate compliance pressure, but less formal governance burden than a regulated enterprise SOC

Bubble size directly correlates to the vendor’s final score in this environment.

The hybrid map rewards vendors that can create a coherent case and act across a mixed stack without demanding a major transformation program. Torq and 7AI lead, with Swimlane close behind after demonstrating production operation across hybrid and federated environments. BlinkOps and Prophet Security also clear both thresholds, with different implementation, reasoning, and maturity tradeoffs. Platform-native vendors fall when their strengths require the buyer to consolidate around their ecosystem, while investigation-first vendors fall when they cannot carry the workflow through governed closure.

Operational Integration and Response Axis

The horizontal axis measures how easily the platform can connect to a mixed environment, assemble actions across systems, and deliver useful response without forcing a major platform replacement. It incorporates integration flexibility, response breadth, deployment simplicity, time-to-value, cross-stack orchestration, and the practical burden placed on a smaller team.

A high score means the vendor can sit above a fragmented stack and turn alerts or cases into a coordinated action. A lower score may reflect platform dependence, heavier implementation, narrower response authority, a requirement for substantial workflow engineering, or insufficient evidence that the product can work efficiently in a hybrid mid-market environment.

Evidence and Analyst Effectiveness Axis

The vertical axis measures whether the product improves the analyst’s ability to reach a defensible decision quickly. It includes cross-system evidence assembly, case quality, transparency, contextual reasoning, productivity gains, useful recommendation, and the ability to reduce console switching and repetitive manual work.

A high score means the product creates a coherent case rather than compiling information to a queue or interface. A lower score may reflect fragmented case objects, weak cross-source correlation, limited reasoning transparency, or an operating model that still leaves substantial review and coordination work for a human analyst.

Balanced Leaders Category

Balanced leaders are the strongest fit for a hybrid environment because they combine practical cross-stack action with strong case assembly and analyst leverage. They do not require the buyer to choose between better reasoning and useful response. The differences among them are primarily in operating model, implementation weight, and maturity.

Torq, 7AI, and Swimlane form the leading hybrid shortlist. Torq and 7AI emphasize speed, flexibility, and reasoning-led operation, while Swimlane offers greater governance and automation maturity with additional administrative weight. BlinkOps remains attractive where workflow flexibility and rapid automation dominate. Prophet Security is attractive when backtesting and controlled progression into action are especially important to buyers.

Analyst-Value Specialists Category

This is the largest category in the hybrid scenario. The vendors ranked here generally improve investigation, evidence quality, or analyst productivity, but have a visible constraint in cross-stack deployment, response breadth, time-to-value, or operating-model neutrality.

The category captures several distinct reasons for falling below the Operational Integration and Response Axis threshold. Palo Alto Networks is highly capable but heavier and more platform consolidation-oriented than an independent mid-market layer. Exaforce is the most powerful when its data architecture can be used to the fullest extent. Mate, Simbian, and AIStrike are promising but have thinner production proof or integration maturity. Radiant, Intezer Qevlar, and Dropzone provide strong analyst value but narrower governed response.

These vendors can be excellent purchases when the buyer’s primary problem is investigation quality or analyst capacity rather than universal automated remediation. The question of diligence buyers should ask is where the workflow stops. Buyers should identify whether the platform directly executes, provides recommendations, triggers existing SOAR playbooks, or relies on a managed service. That handoff determines whether analyst value turns into measurable time-to-closure improvement.

Qualified Contenders Category

The vendors in this category are not low in quality compared to the others. They are less aligned to the hybrid mid-market operating model. They each carry a form of platform weight, workflow dependence, or product fragmentation that reduces both immediate analyst leverage and cross-stack simplicity for a lean team.

Environment 3: Cloud Native Data-Lake SOC

A cloud native organization that does not operate a traditional SIEM and relies on a data lake, warehouse, cloud native log store, or security data platform as the main evidence layer. The company has a small security team, an engineering-heavy operating model, and limited formal regulatory burden. Security work often happens through Slack, Jira, GitHub, cloud consoles, identity tools, and infrastructure workflows rather than a traditional SOC queue.

Generic architecture

• Data lake, warehouse, cloud native log store, or security data platform as the main evidence layer

• Telemetry from cloud logs, identity logs, endpoint tools, SaaS platforms, application telemetry, CI/CD systems, and infrastructure events

• Detection from native cloud rules, open-source detections, EDR alerts, CSPM/CNAPP findings, identity alerts, and custom queries

• Workflow through Slack or Teams, Jira or Linear, GitHub or GitLab, incident channels, and lightweight ticketing

• Response through cloud IAM, IdP, EDR, email security, infrastructure-as-code, secrets management, SaaS admin tools, and cloud consoles

• Small security team with engineering-led response

• Low to moderate regulatory pressure, but meaningful customer trust, uptime, and breach-risk pressure

Bubble size directly correlates to the vendor’s final score in this environment.

The cloud native map rewards products that can work directly with modern data and engineering context without sacrificing case construction or governed response. 7AI and Exaforce lead through evidence and data architecture, while Torq and BlinkOps lead through API-driven action and workflow flexibility. Swimlane clears both leadership thresholds by combining direct and federated telemetry access with mature case management and governed execution. Platform and other SOAR vendors remain credible, but their administrative and consolidation burden pushes them into the Autonomy Specialist category. Investigation first vendors remain contenders until they can prove broader governed execution and outcome verification.

Cloud Native Execution Axis

The horizontal axis measures the vendor’s ability to operate in a no-SIEM or data lake centric architecture. It includes direct data lake access, raw cloud and identity context, engineering-native workflow integration, deployment speed, low administrative overhead, and the ability to function without requiring a traditional SOC process.

A high score means the product can become part of an engineering-led security operating model rather than merely connect to one. A lower score often reflects enterprise platform weight, SOAR-centric administration, dependence on upstream alerts, a requirement for a conventional SIEM or case process, or insufficient evidence that the product can work effectively against federated cloud native data.

Autonomous Case and Response Axis

The vertical axis measures whether the product can autonomously assemble a case, reach a defensible conclusion, and move into useful governed response. It includes no-SIEM case creation, evidence continuity, dynamic reasoning, response usefulness, authority controls, auditability, and outcome verification.

A high score means the platform can do more than query cloud data. It can turn that data into an actionable case and support or execute remediation. A lower score generally means the product is strong at data access or investigation but has narrower response authority, less mature governance, fragmented case continuity, or weaker post-action proof.

Cloud Native Leaders Category

Cloud native Leaders can operate without a traditional SIEM while still providing meaningful case reasoning and response. They are not simply cloud-hosted products. Their architectures support direct or federated access to cloud native data, engineering workflows, and dynamic evidence assembly, while preserving enough authority and governance to move toward closure.

This category is the primary shortlist for teams building a modern no-SIEM operating model. 7AI offers the strongest combined case and autonomy proposition. Exaforce is strongest when data architecture and raw cloud context dominate. Torq and BlinkOps are strongest when API-driven action and workflow flexibility dominate. Mate is differentiated by context quality and control design but requires more production diligence, while Swimlane is differentiated by its ability to apply mature response governance and case continuity to federated, and no-SIEM environments. Although its administrative model remains heavier than the leanest AI-native products.

Autonomy Specialists Category

Autonomy Specialists can provide strong reasoning, case management, governed action, or platform-native response, but are less naturally aligned to a lightweight data-lake-centric operating model. The limitation is usually architectural weight rather than a lack of security capability.

Some vendors in this category can technically replace a SIEM or provide their own data layer, but doing so may require broader platform adoption, higher cost, or more administration than a small engineering-led team wants. Others are strong AI-native products whose integration or production proof is not yet sufficient to clear the cloud-execution threshold.

These vendors are viable when the team is willing to adopt a broader platform or already owns the relevant ecosystem. Palo Alto Networks and CrowdStrike can be excellent choices when consolidation is intentional. Prophet, Simbian, and AIStrike fit teams willing to accept younger production evidence for a more modern reasoning model.

Execution Specialists Category

Radiant Security is the only vendor in this category. It performs well on fast cloud-oriented deployment, native log handling, practical triage, and useful response, but its autonomy-governance model remains less mature than the cloud native Leaders.

Radiant’s placement shows the difference between being operationally convenient in a cloud environment and serving as a fully governed autonomous response control plane. The product can create real analyst leverage and support one-click or selected zero-click actions, but deeper risk-tier policy, multi-stage approval logic, and independent verification of final risk reduction remain less complete.

Radiant can be a strong fit for a small team that prioritizes speed, broad triage, and practical containment over immediate delegation of high-impact actions. The buyer should define which actions can run unattended, how policies change by asset criticality and confidence, how failures are handled, and how the platform proves that the intended risk was actually reduced.

Qualified Contenders

These vendors provide useful capabilities but are less aligned to the complete no-SIEM operating model. Their strengths tend to be concentrated in endpoint response, investigation, forensic analysis, or governed SOAR workflows rather than in the combination of cloud native data access, lightweight deployment, autonomous case construction, and broad response.

Vendor Analysis Findings

The broader market findings describe the common direction of AI SOC development. The comparative vendor analysis reveals how differently products reach that destination and why those differences matter across operating environments. Architecture, integration model, governance heritage, deployment burden, and control-plane ownership consistently influenced vendor placement, often making a product highly suitable for one SOC model and less natural for another.

Architecture has a significant influence on vendor placement and adoption. Palo Alto Networks, CrowdStrike, and SentinelOne benefit in regulated environments where buyers want a unified platform to provide telemetry, decisioning, policy, and enforcement. That same platform dependence creates more friction in hybrid environments where buyers prioritize an independent layer across a fragmented stack. SOAR heritage can introduce administrative and process weight, but it does not inherently limit hybrid or cloud native applicability. Swimlane demonstrates that a SOAR-derived platform can support those environments when it provides direct telemetry access, federated evidence assembly, independent case construction, and production AI reasoning. D3 Security retains more of the traditional SOAR tradeoff because comparable portability and production evidence for its newer AI layer remain less developed.

Modern reasoning and investigation capabilities also need to be separated from enterprise response maturity. Vendors such as 7AI, Torq, BlinkOps, Prophet, Simbian, and Mate demonstrate that AI-native systems can assemble strong cases and support graduated autonomy, but enterprise readiness still depends on production duration, integration breadth, rollback, source-state verification, and deployment evidence. Dropzone, Qevlar, Intezer, and Radiant similarly create substantial analyst value through investigation and evidence assembly, while narrower authority and less mature governance limit their ability to serve as the primary remediation layer. Buyers therefore need to determine whether they are purchasing an autonomous investigator, a governed response operator, or a platform capable of performing both roles.

Outcome verification remains the clearest dividing line across the market. Many vendors can demonstrate that an action was initiated or that an API request was accepted. Far fewer can re-query the source system, prove that the intended state changed, confirm persistence, and connect the result to a measurable reduction in risk. This gap explains why products with strong investigation and action capabilities can remain outside the leading categories. Automated response ultimately requires evidence the execution action worked.

Viewed through the TSRO lens, the market is not divided simply between autonomous and non-autonomous products, or between trusted and untrusted vendors. Vendors differ in the type of authority they can support, the environments in which that authority is credible, and the evidence available to sustain it. AI-native vendors often lead in dynamic investigation and case progression, SOAR-derived vendors often lead in orchestration and governance, and platform vendors often lead where native enforcement and consolidation are acceptable. The strategic question is which architecture can govern the buyer’s required authority portfolio without breaking evidence continuity, accountability, or operational fit. The Trusted SOC is built through that portfolio, not purchased as a designated platform.

Vendor Profiles

The following profiles explain the product architecture, operating strengths, tradeoffs, and market implications behind each vendor’s placement. The evaluation applies only to the AI SOC capabilities and product scope examined in this report. It should not be interpreted as a judgment of the vendor’s overall corporate maturity, financial position, installed base, or broader security portfolio.

Each profile assesses the role a product can play within a Trusted SOC authority portfolio: the actions it can support, the controls surrounding those actions, and the operating environments in which that authority is most credible.

Vendor placement may change across environments because the rankings reward different forms of operational fit. A platform may benefit from native telemetry and enforcement in a consolidated enterprise while imposing greater friction in a heterogeneous or engineering-led environment. A younger AI-native product may provide stronger investigation and analyst experience while carrying less production evidence, narrower execution breadth, or less mature governance.

Torq - Innovator

Vendor Overview

Torq provides an AI SOC platform built around Auto Triage, Case Management, Socrates, Torq HyperAgents™, and security hyperautomation. The company is best understood less as a conventional SOAR vendor with AI features and more as an AI SOC operating layer that can ingest alerts, classify triage outcomes, construct cases, assign work to Socrates, and route remediation through deterministic or agentic workflows depending on the use case.

Torq’s center of gravity is response/action, but the platform has a stronger triage and investigation story than its automation layer alone would suggest. The company reports a 60x increase in triage velocity, a 94% decrease in mean time to respond, and at least one customer where Tier 1 alerts are fully autonomously handled, with a stated path toward automating a larger share of Tier 1 and Tier 2 alert handling by the end of 2026. Those are vendor-provided performance claims and should be treated as validation targets, but they support the core positioning: Torq is trying to own high-volume alert triage and response closure, not simply accelerate analyst review.

Torq’s strongest buyer fit is a mature SOC, MSSP/MDR provider, or large enterprise security team with a heterogeneous security stack and enough historical alert data, integrations, permissions, and workflow maturity to benefit from customer-specific learning, automation routing, and governed action execution.

Product and Architecture

Torq’s architecture is a layered AI SOC and automation system. Auto Triage classifies alerts into false positive, benign, and malicious outcomes. False positives can feed a detection-engineering improvement lifecycle, while malicious alerts escalate into cases. The platform supports more than 50 opinionated triage investigations across common security vendors, as well as a generalist investigator designed to handle a broader range of alert types.

The case object is the operating unit. Torq demonstrated an investigation pattern where entity and business context materially changed the verdict. In the example, a finance analyst contractor using PowerShell to download a file became more suspicious after the system connected endpoint activity to Salesforce evidence showing bulk data export and a report named “All Accounts Spring export.” The important point is that Torq is not treating alerts as isolated artifacts. It is building a context pipeline around entities, access history, business role, device state, SaaS activity, and related evidence before determining whether an alert should be closed, escalated, or remediated.

Socrates is the AI analyst and orchestrator layer. It can be used interactively by analysts, embedded in predefined investigation templates, or assigned cases directly. Once assigned, Socrates can pivot the investigation based on discovered context. Socrates is also positioned as system-wide rather than only case-scoped, with applicability to threat hunts, cross-case analysis, and process planning. This makes Socrates more than a chat interface on top of a case. It is the reasoning and planning layer that decides how much work should be agentic, how much should be deterministic, and where the human should approve or intervene.

The deterministic-versus-agentic split is one of Torq’s sharper architectural claims. Socrates can analyze available integrations and build an EDR triage workflow with one AI step and multiple deterministic steps, explaining why each choice belongs in the workflow. The system asks human-in-the-loop questions at key decision points, including containment aggressiveness and whether actions should be fully automated or human-approved. This makes Torq’s model more precise than generic agentic automation: it is governed composition, where AI plans or reasons when useful, while deterministic workflow logic executes repeatable steps when reliability, cost, and auditability matter.

Torq also emphasizes model-selection discipline and learning architecture. The company uses a weekly model research process across model foundries such as AWS Bedrock, Azure AI Foundry, and Google, with task-level model selection based on human-tagged alert datasets and a metric that balances missed malicious detections against false-positive reduction. The platform also uses two learning systems: a customer-specific encoder/classifier model trained on historical alerts, and an opinionated memory store that preserves alert and case history in a similarity-searchable vector store. Customers can import historical alert records before go-live, which means the system can start with customer-specific context rather than beginning from a blank slate.

Market Context and Positioning

Torq receives an Innovator placement because its architecture connects dynamic evidence gathering, case reasoning, policy-aware authority, and deterministic execution, while mature deployments and broad integrations support strong Production Delivery. Torq is one of the clearest examples of the SOAR-to-AI SOC transition. Its automation heritage gives it execution depth and integration reach, while the current product posture is more substantively AI SOC-oriented around autonomous triage, Socrates-driven investigation, model governance, customer-specific learning, and case-to-action continuity. Compared with AI SOC point solutions, Torq’s advantage is the ability to connect AI decisioning to actual workflow execution. Compared with legacy SOAR, its advantage is that Socrates can reason over cases, plan workflows, and decide which portions of an investigation should be agentic versus deterministic.

The market tradeoff is proof and dependency. Torq’s strongest claims depend on the quality of customer integrations, historical alert data, available APIs, configured permissions, and clearly defined autonomy policies. Torq addresses this concern through auto-routing options such as do nothing, create case, or trigger workflow, and by positioning agentic reasoning and actions as documented, immutable, auditable, and exportable. Buyers should test whether the system can preserve traceability from alert intake to reasoning, workflow decision, approval, action, and outcome.

Torq’s buyer fit is strongest where the organization wants to operationalize autonomy gradually. The idea that autonomy is a dial, not a binary switch, aligns Torq with enterprise adoption patterns. A SOC can begin with triage and recommendation, move into human-approved action, and eventually automate narrower or higher-confidence alert classes. That makes Torq relevant for large enterprises and service providers that need measurable automation without losing control of high-impact response decisions.

Narrative Implication

Torq strengthens the AI SOC report’s argument that autonomous SOC value will be measured by closed-loop operating capability rather than AI summarization. Its product story ties together alert classification, case construction, context-aware investigation, Socrates-led reasoning, deterministic workflow execution, human approval, model selection, organizational learning, and auditable action records.

Torq is an action-centric AI SOC platform with unusually strong automation depth and a more developed learning architecture than a simple AI copilot. Its differentiation is not only that it can automate workflows. It is that it can learn from customer-specific alert history, decide when to use AI versus deterministic logic, assign cases to an AI analyst agent, and preserve proof as work moves from triage to response. Torq is neither legacy SOAR nor pure AI-native analyst replacement. It is better understood as a governed autonomy layer for SOCs that want to turn repeatable analyst judgment into system-owned triage, remediation, and closure.

7AI - Innovator

Vendor Overview

7AI is an AI SOC platform and managed service provider focused on automating investigation, decision support, and response workflows across customer environments. The same technology stack supports multiple consumption models: customers can operate the platform with their own SOC team, consume 7AI as a managed service through PLAID ELITE, or use the platform with MSSP and MDR partners.

The company’s positioning is strongest around the final mile of SOC work. 7AI’s argument is that the hardest operational question often comes after the case is assembled: what happens next, who approves it, and how much authority the system receives. The platform moves from evidence to action through customer-defined authority levels, including recommendation, approval-required execution, supervised execution, and policy-gated automation for lower-blast-radius actions. Action plans are generated from assembled case evidence and organizational context, while confidence and policy controls determine whether an action is recommended, queued for approval, or executed. Failed or higher-impact actions can follow defined retry, escalation, and action-specific recovery paths.

Product and Architecture

7AI’s architecture spans detection optimization, agentic investigation, threat hunting, organizational context, workflow automation, and optional managed service delivery. Detection Optimization analyzes rules across connected sources, identifies noisy or incomplete coverage, recommends tuning, and maps detection coverage to MITRE ATT&CK. The platform connects to security tools, consumes alerts and evidence, runs investigations through task-specific agents, and exposes agent reasoning. The product shows the mission assigned to each agent, the inputs used, tools called, requests made, responses received, and conclusions reached.

Enterprise Insights is the most important context feature. It allows customer-specific knowledge, including user roles, accepted tools, disallowed software, business processes, and known behavior patterns, to influence investigation and response decisions. That matters because the same signal can have different meaning across organizations or business units.

Threat Hunting supports analyst-directed, natural-language hunts and threat-intelligence-driven hunts across connected customer telemetry. Analysts can start with a hypothesis, behavior, or ATT&CK technique, while incoming intelligence can initiate hunts as new indicators become available. Separately, the Skills framework lets customers create, test, and reuse investigation, hunting, and automation routines tailored to their environment.

The integration story is broad, with the platform designed to connect across security and IT systems. Integration depth determines response value because actions depend on API quality, permissions, telemetry freshness, identity mapping, and available write scope. After an action executes, 7AI can re-query the source system to confirm the resulting state and record submission, execution, and verified completion separately in the case. Federated search is an important direction because it lets the platform search connected tools without requiring all data to be copied into a central SIEM.

Market Context and Positioning

7AI receives an Innovator placement because its architecture connects unified case construction, transparent reasoning, graduated authority, and conclusion-driven response, while named production outcomes support strong Production Delivery despite a younger response catalog than the mature orchestration leaders. 7AI fits the AI SOC market as a hybrid platform and service model for organizations that want investigation automation tied to action. Its competitors include AI SOC point solutions, MDR modernization providers, SOAR and automation platforms adding AI, and security platform vendors embedding autonomous response into their own control planes.

7AI’s market position combines cross-stack neutrality, organizational context, transparent agent reasoning, and flexible delivery across software, managed service, and partner-led models. Its architecture begins with agentic investigation and extends into governed response, while detection optimization and threat hunting create feedback loops that can improve future case quality. Hunting findings can inform detection coverage, and investigation or response outcomes can support subsequent rule tuning. The value of this model depends on integration depth, customer-specific context, and how consistently those feedback loops operate in production.

Narrative Implication

7AI sharpens an important point for the AI SOC report: the final mile of SOC automation is organizational judgment encoded into a repeatable workflow. The platform is designed around the idea that the SOC needs a system that can learn customer context, gather evidence, explain reasoning, suggest or execute next steps, verify the resulting state, and let humans decide where automation should stop.

The product’s publication story is delivery flexibility. 7AI can be consumed as software, service, or partner-enabled capability, making it relevant to buyers that want agentic SOC outcomes but differ in how much operational responsibility they want to retain internally.

Palo Alto Networks - Trailblazer

Palo Alto Networks is assessed in this profile only against the AI SOC capabilities available within the Cortex platform. Its placement does not reflect Palo Alto Networks’ overall cybersecurity market position or the breadth and maturity of its broader product portfolio.

Vendor Overview

Palo Alto Networks’ AI SOC proposition is centered on Cortex XSIAM and is informed by the company’s long history of building enterprise security tools and platforms. Palo Alto Networks frames AI as an important new capability within a 20-year security history of engineering security platforms. That maturity matters: the platform is not presented as a standalone AI assistant, but as an operational SOC environment shaped by enterprise security controls, scalability requirements, governance, playbooks, case management, and analyst workflow design.

The relevant product scope for this report includes XSIAM, Cortex XDR heritage, Demisto-derived SOAR and playbook capabilities, case management, automation, and data ingestion across Palo Alto and third-party sources. Palo Alto Networks’ core narrative is that the AI SOC must be a full-stack operating platform rather than a thin AI overlay. In practice, this means collection, normalization, enrichment, detection, grouping, risk scoring, case investigation, root-cause analysis, and remediation workflows are intended to sit in a common console and decision loop.

Palo Alto Networks is deliberately introducing automation gently rather than forcing an abrupt move to full autonomous response. The XSIAM command center highlights cases that “could have been automated,” which is designed to show SOC teams where they could have benefited from more automation and automatic response without immediately removing human control. This experience supports the twin objectives: reducing confirmed MTTD toward less than 10 minutes and reducing MTTR toward a similar single-digit-minute target, with the latter acknowledged as aspirational for many SOC teams because it depends on trusted playbooks and AI agents, permissions, approvals, and operational confidence.

Product and Architecture

XSIAM can be described as a three-layer operating model. The data layer ingests and normalizes evidence from endpoints, firewalls, network flows, identity systems, cloud sources, Cortex telemetry, and selected third-party EDR and telemetry providers, as well as any other data source. The decision layer correlates and clusters that evidence into cases, applies analytics, performs risk scoring, and provides AI-supported investigation. The execution layer uses playbooks, SOAR actions, case management, and agentic workflows to move from understanding an incident to taking approved action.

A key architectural differentiator is Palo Alto Networks’ emphasis on the complete SOC pipeline. The company positions XSIAM Command Center as a unified view across ingestion, normalization, detection, grouping, scoring, and case management. The case investigation view then adds AI-generated summaries, attack-chain visualization, next-step recommendations, agentic Q&A, and transparent explanation of why alerts have been grouped. This reinforces XSIAM’s role as a full-stack platform for collection, normalization, investigation, root-cause analysis, and remediation rather than a disconnected AI layer bolted onto existing SOC tooling.

Palo Alto Networks also states that AI SOC and the data platform must converge. Its position is that detection, investigation, and response quality depend on normalized, enriched, contextualized data before an incident occurs. The company supports federated search where appropriate, but its stated belief is that a core baseline dataset is necessary for high-fidelity detection and response. It also emphasizes that anomalous behavior alone is not proof of malicious activity; security research expertise and data science need to be combined to determine whether deviations from baseline are actually meaningful.

Palo Alto Networks’ platform is best suited for large enterprises and advanced mid-market organizations with mature SOC operations, high telemetry volumes, and complex, globally distributed environments. Its SaaS-only model is designed to handle large-scale data processing, analytics, and AI-driven automation, making it less suitable for organizations without dedicated security teams. With 26 global hosting regions, including federal and sovereign options, it is particularly well aligned to Fortune-scale and regulated customers that require strong data residency support and the ability to operate across multiple jurisdictions and distributed SOC teams.

Palo Alto Networks positions automation as a spectrum ranging from deterministic workflows to hybrid AI-assisted playbooks and fully agentic operations, emphasizing gradual adoption rather than immediate full autonomy. The platform defaults to human approval for sensitive actions and uses guardrails such as RBAC/SBAC and cost controls, while features like the “could have been automated” view help SOC teams build trust through repeated validation of automation outcomes. This staged, evidence-based approach aligns with customer hesitation around fully autonomous response and is reinforced by support for customer-built agents and external AI tools, enabling organizations to start with familiar processes and progressively expand automation as confidence grows

Market Context and Positioning

Palo Alto Networks receives a Trailblazer placement because Cortex demonstrates mature governance, native enforcement, source-state verification, and enterprise-scale Production Delivery, while complete lifecycle ownership remains distributed across the broader platform and its newest agentic capabilities are still maturing. Palo Alto Networks is positioned as a major platform vendor applying AI-enabled operations to the historical SOC stack. The competitive frame includes SIEM modernization, XDR expansion, SOAR consolidation, AI copilots, MDR-led operating models, and AI-native SOC entrants. XSIAM’s market relevance is strongest where the buyer wants a consolidated security-operations platform that can absorb more of the SOC decision loop rather than a narrow AI assistant or investigation overlay.

The Cortex XSOAR foundations remain an important part of this positioning because response orchestration is the point at which AI SOC capabilities move from investigation support into operational execution. In the XSIAM model, SOAR becomes less of a separate automation tier and more of an embedded execution mechanism within the SOC workflow. The demo’s emphasis on shift-change automation, AI-enabled playbook steps, and case handoff illustrates how Palo Alto Networks is using automation not only for response actions, but also for routine operational burden reduction.

The strongest differentiation is therefore not simply “AI in the SOC,” but the combination of enterprise platform depth, data normalization, integrated investigation, human-governed automation, and scaled deployment options. Palo Alto Networks is also making the analyst experience transparent: AI-generated summaries, attack-chain graphs, recommendations, and explanations are used to orient analysts and accelerate action without hiding the reasoning behind case construction.

Narrative Implication

Palo Alto Networks illustrates how the AI SOC category can be shaped by consolidated security platforms as well as AI-native vendors. XSIAM aligns with SACR’s category definition where AI SOC is understood as an operating model for the SOC decision loop rather than a chat interface. The solution applies AI across detection, investigation, explanation, automation, and response, while preserving enterprise guardrails and gradual human-in-the-loop adoption.

Palo Alto Networks’ platform is most suitable for very large, geographically dispersed customers with high data volumes; its 26-region hosting footprint including federal and sovereign options; and its full-stack approach to collection, normalization, investigation, root-cause analysis, and remediation are significant here. The distinctive approach to automation adoption: guiding teams toward more automation over time, building confidence through repeated iterations of specific SOC use cases, and allowing customers to use their own AI tools or agents are sure to improve trust and acceptance.

BlinkOps - Innovator

Vendor Overview

BlinkOps is an Agentic Security Operations Platform that packages AI SOC and SOAR capabilities as part of a broader security automation environment. The platform is organized around Workflow Studio, Agent Studio, and Solution Studio. Workflow Studio is the deterministic workflow layer, Agentix Studio is the agent-builder layer, and Solution Studio is the user-facing application, dashboard, and case-management layer.

BlinkOps is best understood as an AI-enabled security automation platform rather than a single-purpose AI SOC product. AI SOC is one capability area within a broader platform that also addresses SOAR, vulnerability management, cloud security, and other security program workflows. The strongest buyer fit is a team that wants to automate across an existing stack rather than replace core detection, SIEM, EDR, or case-management systems.

Product and Architecture

BlinkOps uses a layered operating design. Deterministic workflows handle ETL-style tasks, structured checks, and initial triage. A hybrid layer uses classic statistics, machine learning, and smaller models for enrichment, routing, and context assembly. Larger reasoning models are reserved for complex synthesis or novel situations.

The platform’s operating loop can be summarized as See, Understand, Decide, and Act, with a feedback arc that can revisit investigations as new evidence appears. BlinkOps separates decisioning from execution. A verdict can be challenged before an action proceeds, and agents operate with scoped abilities defined by customer roles, responsibilities, and policy constraints.

Human review is the default operating posture. Autonomy is earned over time as agent performance is observed and trusted. The platform starts with deterministic behavior and can increase autonomy as workflows mature. For situations outside known patterns, a novelty branch routes the work through a different reasoning path and updates future investigation artifacts.

BlinkOps’ scale claims, including integration counts and microagent counts, reinforce the platform’s breadth, but the key architectural issue is practical action depth: which connectors can gather evidence, which can change state, and how each action is governed, logged, retried, or reversed.

Market Context and Positioning

BlinkOps receives an Innovator placement because its architecture connects specialized agents, deterministic workflows, configurable authority controls, and broad action reach, while mature automation use supports strong Production Delivery despite less specific deployment evidence for the Agentic SOC layer. BlinkOps fits the SOAR + AI and security automation branch of the AI SOC market. It is relevant where buyers want AI SOC capabilities embedded in a workflow, automation, and orchestration platform rather than in a standalone AI analyst product. Its competitive set includes SOAR automation vendors, AI SOC point solutions, MDR-adjacent AI SOC vendors, and broad security platforms adding AI-powered SOC features.

BlinkOps’ go-to-market theme is augmentation of existing tools. The company helps customers identify, orchestrate, and improve workflows across the tools they already use. That makes BlinkOps strongest where a buyer has many fragmented tools, established workflows, and a desire to increase automation without rip-and-replace.

Narrative Implication

BlinkOps shows that AI SOC may become an automation capability embedded inside broader security operations platforms. Its strongest narrative is layered automation: deterministic workflow, scoped agents, human review, novelty handling, and progressive autonomy.

The product’s long-term relevance depends on proof at the action layer. The most important question is not whether BlinkOps can build workflows, but whether those workflows preserve evidence, challenge decisions before execution, handle failure, verify state change, and give teams enough control to trust automation in production.

Swimlane - Innovator

Vendor Overview

Swimlane Turbine is an agentic AI automation platform that sits above existing security stacks. It ingests alerts and context from customer-owned tools, converts them into durable case state, drives governed response actions, and preserves proof for analysts, managers, and auditors. Depending on the buyer’s starting point, Swimlane can serve as an AI SOC operating layer or as a path to modernize an existing SOAR program with AI.

This matters because many buyers will adopt AI SOC by upgrading the orchestration and action layer they already trust. Swimlane’s strongest fit is the enterprise, federal, regulated, and MSSP/MDR buyer that needs cross-tool execution, repeatable workflow control, progressive autonomy, and audit-grade reconstruction.

Product and Architecture

Swimlane Turbine’s architecture is best understood as three connected layers: a federated data fabric for querying customer-owned sources, Hero AI agents for investigation and workflow generation, and the case management and orchestration layer for execution and proof. The platform depends on customer telemetry for detection input, while Turbine can become the system of record for investigation, decision, action, and closure.

The case object is the most important architectural primitive. Swimlane describes the case as the durable work unit from evidence intake through enrichment, investigation, recommendation, approval, execution, verification, and closure. Each state transition is intended to remain visible in the case record, including agent decisions, human review, approval, execution, failure, retry, rollback, and final outcome.

The action surface is broad by AI SOC standards and mature by SOAR standards. Swimlane reports response coverage across endpoint, identity, cloud/runtime, email/collaboration, ITSM/workflow, network, and custom APIs. The buyer implication is straightforward: Swimlane can reach many control points where the customer has integrations, credentials, policies, and permissions configured.

Governance is central to Swimlane’s credibility. Agent identities are governed through RBAC, with agents authorized only for specific data and tools. Policy decisions can consider confidence, evidence completeness, asset criticality, user role, privilege level, business unit, action type, blast radius, incident severity, time window, and exception state. Analysts can edit, pause, reject, override, or escalate AI-generated action plans before or during execution.

Market Context and Positioning

Swimlane receives an Innovator placement because it combines mature orchestration, governance, case continuity, rollback, and enterprise-scale execution with demonstrated production AI reasoning and the ability to operate across SIEM-centric, hybrid, and no-SIEM environments. Its federated data architecture allows Turbine to assemble evidence directly from customer security and cloud systems, while Hero AI connects that evidence to investigation, recommendation, governed action, and durable case records. Swimlane reaches the Innovator category through a SOAR-derived architecture, demonstrating that architectural heritage does not determine leadership when the product can connect the AI SOC lifecycle and deliver it credibly in production.

The tradeoff is proof. Buyers should not reward AI labeling on top of conventional SOAR without case-to-action evidence. The strongest evaluation asks what evidence the agent used, what action it recommended, what policy applied, who approved it, what executed, what state changed, what failed, what rolled back, and what remained open.

Narrative Implication

Swimlane broadens the AI SOC narrative. The market will be built by AI-native startups and by automation platforms that already own response plumbing and can use AI to reduce the distance between evidence, decision, action, and proof.

The product’s strongest publication story is trustworthy execution. Swimlane can bring AI into operational workflows without abandoning deterministic controls, approval paths, analyst override, or auditability. That makes it an important reference point for buyers that want autonomous security operations to evolve from governed automation rather than from open-ended reasoning alone.

Exaforce - Innovator

Vendor Overview

Exaforce’s core bet is that autonomous SOC response requires a real-time model of the environment rather than an LLM layered over alerts. The platform is built around a knowledge graph that combines alert data, configuration state, identity behavior, session context, and connected system activity. The broader platform spans detection, triage, investigation, and response, with Exabot Respond serving as the action-oriented component that carries the context assembled earlier in the lifecycle into governed execution.

Exaforce is best positioned as a context-driven AI SOC platform with response capability, not as a generic AI assistant. Its differentiation comes from connecting evidence, context, policy, and scoped action inside a single operating model.

Product and Architecture

Exaforce’s operating model is built on a knowledge graph that combines alert data with configuration, identity, session, and behavioral context. The graph feeds a multi-phase reasoning workflow covering classification, planning, analysis, generation, and validation. The output is a structured verdict with plain-English rationale and action plans. Business context rules allow customers to encode constraints such as protected user groups or business units at the planning stage.

Exabot Respond is the response-oriented task agent within the broader Exaforce platform. It uses the evidence, entity relationships, behavioral context, and verdict developed during detection and investigation to generate and execute response actions. The response model includes stateful workflows, retry policies, escalation when connectors are unavailable, timeout and escalation chains for unavailable approvers, and case records that remain open until actions resolve.

Common automation use cases include user confirmation workflows, session revocation, password resets, ticketing, and email actions. Exabot Respond can execute across endpoint containment, identity session revocation, cloud isolation, email purge, and custom API actions where configured. Customers can tailor response behavior through configurable workflows, action catalogs, business-context rules, approval points, and custom API actions. Approval requirements are governed by policy.

Exaforce’s response strength comes from applying investigation context and policy to configurable actions across multiple control domains. Granular session control is a concrete example of that precision: the platform can terminate higher-risk sessions while preserving lower-risk activity where appropriate. This reduces exposure while limiting unnecessary business disruption. Source-system state confirmation strengthens the model by checking whether the expected change occurred before a risk finding is closed. Automated rollback is less mature than forward execution, leaving manual inverse actions and compensating controls important for recovery.

Market Context and Positioning

Exaforce receives an Innovator placement because its architecture connects a real-time evidence model, defensible reasoning, policy constraints, and source-aware response, while credible production use supports delivery and action-dependent rollback remains the principal maturity constraint. Exaforce sits between SIEM incumbents applying AI to log data, AI SOC specialists focused on triage and investigation, and SOAR platforms extending playbook automation with AI reasoning. Its market argument is that response reliability depends on context depth, policy constraints, and scoped action precision rather than reasoning quality alone.

The platform works with existing tools, including SIEM environments, and can pass verdicts and context back to originating systems. Customers can retain their current detection investments while using Exaforce to add contextual detection, triage, investigation, and governed response across the existing stack. The strongest buyer fit is a security team that wants broader case-to-resolution automation without replacing its established control and telemetry layers.

Narrative Implication

Exaforce reinforces a broader AI SOC pattern: reliable response depends on the quality and continuity of the work performed before an action is selected. Detection, triage, investigation, organizational context, policy, and execution operate as parts of one case-to-resolution system. Exaforce’s contribution is the connection between that broader operational coverage and governed response, with granular session control, scoped blast-radius controls, and graduated deployment providing concrete examples of how autonomy can be constrained.

Exaforce’s publication story is strongest when framed around comprehensive case context, configurable execution, precision, and governance. The platform has a credible path to owning the last mile of response where customers can supply the integrations, permissions, policies, and audit requirements needed to support controlled execution.

Prophet Security - Innovator

Vendor Overview

Prophet Security provides an agentic SOC platform centered on Prophet AI SOC Analyst, with adjacent capabilities for AI Threat Hunter, Watchtower human oversight generally available, and Detection Engineer functionality on the product roadmap. The platform is designed for enterprise SOC teams that want to increase investigation coverage, reduce manual triage, and introduce governed remediation without replacing their existing SIEM, EDR, identity, email, cloud, ITSM, or collaboration systems.

Prophet’s primary AI SOC control point is detection and decision with response extensions. The platform meets customers where their telemetry and workflows already live. It is not positioned as a SIEM migration platform, data lake product, MSSP operating console, or full SOAR replacement.

Product and Architecture

Prophet AI SOC Analyst ingests alerts, builds and executes investigative plans, queries connected tools, groups related findings into investigations or cases, dispositions activity, and recommends or executes Agent Actions. Prophet uses frontier models rather than training a proprietary foundation model. The product differentiation is investigative depth, evidence transparency, and an analyst-oriented user experience.

The platform does not require customers to replicate or migrate data into a new SIEM. It queries evidence where it resides, including SIEM, Databricks, Snowflake, S3, EDR, identity, email, SaaS, cloud, network, and ITSM sources. Prophet emphasizes API-based integration, arbitrary webhook ingestion for custom alert sources, and Prophet-built retrieval agents where raw API-query traceability and auditability are required. MCP can support some technology classes, while Prophet’s own retrieval layer is more relevant where transparency and accuracy are critical.

Agent Actions are the response layer. The product supports natural-language workflow creation, translation into deterministic branches and REST API calls, autonomy settings across autonomous, approval-required, and recommend-only modes, and rate limits to restrict action frequency. Backtesting allows customers to see which historical investigations would have triggered an action before enabling it in production. Actions can cover identity, endpoint, cloud, email, and ITSM or workflow domains, with enforcement executed through customer-owned tools.

Prophet AI Threat Hunter extends the platform into autonomous hunting with prebuilt MITRE ATT&CK hunts, custom natural-language hunt creation, scheduled reporting, remediation actions, and a threat-researcher agent that monitors emerging threats. Watchtower is an optional 24x7 human oversight service that can review selected investigations, add analysis, tune agents, orchestrate remediation, and provide daily rollups.

Market Context and Positioning

Prophet Security receives an Innovator placement because its architecture connects contextual investigation, governed action, backtesting, rate limits, and source-aware verification, while current product evidence supports Production Delivery and broader independent deployment proof remains the primary diligence item. Prophet sits in the AI SOC segment as an investigation and governed response layer around existing SOC tooling. Its most natural buyer is an enterprise SOC with existing telemetry, established workflows, and a desire to improve coverage and response consistency without replacing the current operating model.

The company’s go-to-market focus is the enterprise segment, particularly large organizations that want to control outcomes directly. Prophet is not primarily positioned around MSSP partnerships. Watchtower provides a managed oversight option, but the product’s primary market posture remains enterprise-direct.

Narrative Implication

Prophet represents a case- and evidence-centered AI operating layer that connects alert intake, investigation, decisioning, governed action, and proof. Its strongest claims are durable investigation objects, chain of custody from evidence to action, source-system action verification, rollback where supported, policy constraints, approval handling, and operational metrics.

The product’s publication story is case-to-action discipline. Prophet’s decisioning and orchestration are native, while enforcement occurs in customer-owned tools. Completeness therefore depends on connector availability, API permissions, source-system capabilities, and the customer’s willingness to define autonomy boundaries.

Mate Security - Pioneer

Vendor Overview

Mate Security’s core bet is that AI SOC outcomes degrade when environment understanding is treated as an afterthought. The platform is built around a Security Context Graph that captures architecture, ownership, SOPs, investigation history, and operational nuance, then uses that graph as the substrate for investigation and decision support.

Mate positions itself on the augmentation path. The vendor expects SOC roles to evolve toward a more engineering-led operating model, where detections and agents are iterated and benchmarked over time. The platform aims to increase analyst throughput and decision quality while keeping humans in the loop for business-impacting response decisions.

Product and Architecture

Mate’s platform is anchored in a Security Context Graph designed to learn a customer environment the way a new analyst would, but faster and more systematically. It ingests and continuously updates context from SIEM and data lake schemas, ticketing system case history, Slack incident channels, onboarding guides, SOPs, architecture diagrams, and cloud segmentation configurations.

The output is not just a static knowledge base. Mate organizes context into structured primitives such as memories, playbooks, architecture maps, and policies. Investigation is graph-first and transparency-forward. The platform includes an investigation queue and case-management interface with AI-generated summaries, prioritization, and suggested response actions. A browser extension can overlay context and task guidance inside third-party tools, allowing analysts to review collection steps, user profiling, and tool queries without switching platforms.

Mate has expanded into detection engineering and threat hunting. The product centralizes detections spread across tools, tracks coverage and quality, and supports federated execution where detections and hunts run against data at the source rather than requiring full SIEM ingestion. Analysts can initiate a hunt by providing a threat-intelligence report or a free-text hypothesis. Mate extracts relevant indicators and techniques, searches connected tools, correlates the results, and can translate the findings into detection content. Mate frames this as a hybrid stance that recognizes compliance and near-real-time requirements still require hot storage for some use cases.

Mate provides governed remediation capabilities such as session revocation, password reset, email purging, false-positive closure, and emerging device isolation. Response actions are governed by the CPR model: Confidence, Precision, and Rollback. Production use is staged, with lower-impact actions more broadly suited to automation and user-account or device-level actions adopted more cautiously.

Market Context and Positioning

Mate Security receives a Pioneer placement because its Security Context Graph, constrained tools, judge agents, and execution-time validation create strong Architectural Alignment, while limited deployment history and downstream-dependent rollback keep Production Delivery in a developing stage. Mate sits in the detection and decision band of the AI SOC landscape, competing with vendors that pair investigation quality with governed remediation. Its differentiation is the context substrate. Mate argues that many vendors are beginning to talk about context, but that context must be foundational and continuously refreshed to support defensible AI decisions.

The platform also intersects with detection engineering, investigation automation, response guidance, and supervised remediation. Its comparison set includes AI SOC point solutions, context-first investigation platforms, and SOAR + AI vendors that rely more heavily on deterministic workflow automation.

Narrative Implication

Mate reinforces a core AI SOC pattern: the limiting factor is often whether the system can reliably acquire and apply environmental context without burdening teams with manual knowledge engineering. The strongest AI SOC approaches are converging on an explicit context substrate as the prerequisite for trustworthy decisions.

The product’s publication story is context-led trust. Mate is strongest where buyers believe AI SOC requires a durable model of the environment, not just faster alert investigation. Its remediation path remains governed, staged, and aligned to customer SOPs rather than framed as broad fully autonomous execution.

Simbian - Pioneer

Vendor Overview

Simbian positions itself as an AI-native first responder for security operations. The platform sits alongside the existing stack rather than replacing the SIEM, with the goal of compressing the alert lifecycle from triage into decision and action without forcing teams through a heavy playbook-authoring exercise.

The company’s center of gravity is an agent family tied to a shared reasoning engine and context lake. Simbian’s pitch is that the SOC functions better as a connected decision system that learns from analyst feedback and repeated patterns than as a collection of disconnected automations.

Product and Architecture

Simbian uses a reasoning engine that runs investigations end to end, selects and cross-checks models to reduce hallucination risk, and uses contextual knowledge to reach a defensible conclusion. The platform is organized around multiple agents that share the same underlying reasoning engine and context substrate. The AI SOC agent is the common anchor, while peer agents such as threat hunting and pen testing can feed and consume shared context.

The product performs alert-to-case normalization across a heterogeneous tool layer. It aggregates alerts across common sources such as SIEM, EDR, identity, and cloud, with the goal of completing many investigations before an analyst begins work. For MDR-style delivery, the value is partly translation and normalization across heterogeneous customer stacks.

Simbian’s autonomy model is staged and selectable. The vendor distinguishes “human in control” from strict human-in-the-loop gating. Autonomy can be configured by environment, alert type, user group, and confidence threshold. The adoption path starts with read-only operation, moves into auto-close for false positives, then notifications, then containment. Containment is intentionally scoped toward reversible actions as the default safety posture.

The Context Lake is the self-improvement substrate. It stores supplementary organizational knowledge and feedback rather than raw logs. It can incorporate SOPs, travel schedules, organizational constraints, and agent-discovered patterns, then reuse those patterns to reduce repeated work and improve decision consistency.

Market Context and Positioning

Simbian receives a Pioneer placement because its architecture connects AI-native reasoning, durable cases, customer-controlled authority, and bounded execution, while independent production evidence and source-system validation remain less mature than the breadth of its capability claims. Simbian sits between triage relief, governed containment, and MDR-compatible AI SOC delivery. It is strongest for environments where humans cannot investigate everything, where outcomes are measured by closure rate and time to disposition, and where buyers want a staged path from investigation into containment.

Simbian is structurally compatible with MDR delivery through RBAC based multi-tenate architecture, and normalized heterogeneous tool stacks and autonomy tuning by customer. Its market role depends on whether buyers want AI SOC as a first-responder system that works across existing tools, learns from local context, and gradually assumes more operational authority.

Narrative Implication

Simbian is a strong example of the AI SOC market moving from investigation assistance into an execution-oriented operating layer. The important signal is the operating model: a unified reasoning engine and durable context substrate, paired with an autonomy progression that starts in read-only and can move toward containment under customer policy.

The product’s publication story is staged operational trust. Simbian does not need to claim immediate full autonomy to be relevant. Its value comes from converting repeated investigative work into context-aware response patterns while keeping containment scoped, reversible, and adjustable by customer policy.

CrowdStrike - Emerging Player

CrowdStrike is assessed in this profile only against the AI SOC capabilities available within the Falcon platform. Its placement does not reflect CrowdStrike’s overall cybersecurity market position or the breadth and maturity of its broader product portfolio.

Vendor Overview

CrowdStrike positions the Falcon platform as the foundation for its AI SOC strategy, integrating AI, telemetry, SIEM, automation, and response into a single security operations platform. The relevant product scope includes Falcon Next-Gen SIEM, Charlotte AI (including Charlotte AI AgentWorks, and Charlotte AI Agentic SOAR), managed detection and response services, strategic advisory services, and adjacent Falcon controls across endpoint, identity, cloud, browser, data protection, exposure management, and threat intelligence.

CrowdStrike represents the platform-consolidation path for AI-enabled security operations. Falcon combines native telemetry with data and controls from the customer’s broader security stack, providing a common environment for visibility, detection, unified case management, investigation, workflow coordination, and response. This extends Falcon’s SIEM, AI, automation, and response capabilities into a broader operating model for SOC modernization, although the degree of continuity across the environment depends on integration depth, licensing, and customer configuration.

Product and Architecture

The Falcon architecture can be understood as a three-layer operating model. The onboarding layer collects and prepares data through the Falcon sensor integrations and data-pipeline capabilities. The operational layer uses Falcon Next-Gen SIEM and Charlotte AI capabilities, as well as customer-defined knowledge sources to support triage, investigation, recommendation, and workflow construction. The orchestration layer uses Charlotte Agentic SOAR capabilities, and integrations to coordinate actions across first-party modules and third-party systems.

CrowdStrike’s strongest AI SOC relevance is in cross-layer telemetry, case-centered investigation, governed automation, human-in-the-loop approval, traceable agent execution, managed detection, and integration breadth. The platform benefits most where Falcon already serves as a major control plane, because endpoint, identity, cloud, threat intelligence, SIEM, MDR, and response context can be brought into a common operating environment.

Data-pipeline and acquisition-driven capabilities strengthen the broader architecture when they are integrated into Falcon workflows. Falcon Onum and related data movement, enrichment, transformation, routing, monitoring, and reduction capabilities are relevant to the AISOC data foundation. Other adjacent modules can expand identity, cloud, browser, AI security, exposure management and data protection coverage. The degree of operational continuity depends on module licensing, integration maturity, deployment scope, and customer configuration.

CrowdStrike can support recommendation, governance, coordination, and execution with operator-defined autonomy. The operator may be the customer or CrowdStrike’s managed service, depending on the deployment model. Closed-loop risk-reduction verification, multi-tier approval chains, and cross-module action depth vary by use case, licensed capabilities, deployed Falcon modules, integration depth, and customer policy.

Market Context and Positioning

CrowdStrike receives an Emerging Player placement because Falcon provides mature native enforcement, governance, and production reach, while the complete cross-stack reasoning, case-continuity, verification, and rollback chain remains less fully connected across the broader AI SOC lifecycle. CrowdStrike is the platform-native consolidation path in the AI SOC market. Its competitive frame includes SIEM vendors adding AI, XDR vendors expanding into cross-domain operations, SOAR platforms adding generative workflows, MDR providers using AI to scale service delivery, and AI SOC point solutions. Its differentiation lies in the breadth of Falcon-native telemetry, threat intelligence, managed detection, unified case handling, response control points, data-pipeline capabilities, and ecosystem integrations.

The strongest fit is an organization that already uses Falcon extensively or wants to consolidate security operations around a common data and response platform. Large and technically mature enterprises are best positioned to operate the broader architecture directly, while smaller or resource-constrained teams can access portions of the model through Falcon Complete MDR and strategic services. Broad internal AI SOC adoption still requires maturity around data-source onboarding, policy design, workflow governance, connector management, standard operating procedure maintenance, and agent-trace review.

Narrative Implication

CrowdStrike shows how the AI SOC category will be shaped by major platform vendors as well as AI-native startups. A platform-native approach can connect telemetry, threat intelligence, unified case management, AI-assisted reasoning, orchestration, managed services, and response controls inside a broader security operating environment. This gives AI agents access to more consistent context than they would receive when operating independently across fragmented tools.

The tradeoff remains dependence on platform footprint and integration maturity. CrowdStrike’s strongest AI SOC value emerges when Falcon is central to the customer’s SOC architecture. In more fragmented environments, realized value depends increasingly on data onboarding, integration depth, workflow design, licensing, and governance discipline.

AIStrike - Pioneer

Vendor Overview

AIStrike positions itself as an AI-native security operations control plane that sits above existing SIEM, EDR, cloud, identity, and ITSM systems. The company’s core argument is that the SOC bottleneck has shifted from detection and summarization toward triage, investigation, and action. AIStrike therefore frames AI SOC as a closed-loop operating model: operationalize threat intelligence and environment context, produce defensible case decisions, drive governed response actions, and feed outcomes back into continuous improvement.

AIStrike’s center of gravity is a hybrid of detection engineering, AI-powered investigation, and response automation. Autonomous response becomes more trustworthy when upstream detection posture is improved: coverage gaps are identified, noisy rules are tuned, and investigation context becomes consistent enough that action recommendations are repeatable.

Product and Architecture

AIStrike uses an AI orchestrator that generates a runbook across enrichment, prioritization, investigation, and response, with each step visible and auditable. The platform combines external threat intelligence with customer context such as assets, identities, vulnerabilities, misconfigurations, and historical case data. That context supports case grouping and pattern recognition rather than treating alerts as isolated units.

The product story is strongest around case-to-action orchestration. AIStrike generates response plans from investigation context rather than relying only on static, pre-authored playbooks. The action surface includes notification and workflow systems, ticketing systems with bidirectional updates, and state-changing containment or identity actions such as endpoint isolation, process termination, password reset, token revocation, and IP or hash blocking where configured.

AIStrike’s autonomy model is tiered by action impact. Low-risk or clear-cut actions can move toward human-on-loop operation, while high-impact actions require explicit approval. Timer-based autonomy allows AIStrike to schedule an action, notify the analyst, and proceed if no one intervenes within a defined window. This creates a governed response mechanic rather than blanket hands-off remediation.

Detection posture improvement is part of the architecture. AIStrike produces detection health outputs that identify coverage gaps, silent rules, noisy rules, and ready-to-deploy fixes. This improves the upstream conditions that make downstream automation safer.

Market Context and Positioning

AIStrike receives a Pioneer placement because its architecture connects knowledge-graph context, detection feedback, risk-tiered authority, and broad response claims, while uneven live evidence across representative action families keeps Production Delivery in a developing stage. AIStrike sits between AI triage and investigation vendors, SOAR and workflow vendors, and platform-native SIEM/XDR suites. Its differentiation is the claim that action becomes defensible when the system understands threat intelligence, customer context, and detection posture together.

The market fit includes enterprise SOCs that want efficiency and consistency without replacing the existing stack, and MSSP/MDR providers that want higher operational throughput. The enterprise buyer will likely focus on policy, approval, auditability, rollback, and governance. Service-provider buyers may emphasize throughput and repeatability.

Narrative Implication

AIStrike pushes the AI SOC narrative toward action-capable AI SOC rather than better summarization. Its product story is built around case management, case-to-action continuity, and visible orchestration across enrichment, prioritization, investigation, and response.

The product’s publication story is that closed-loop response depends on the quality of the upstream decision system. AIStrike is strongest where it can connect threat intelligence, customer context, detection health, investigation evidence, and governed response into a single operating path.

Stellar Cyber - Emerging Player

Stellar Cyber is assessed in this profile only against its AI SOC capabilities available within its platform. Its placement does not reflect Stellar Cyber’s overall cybersecurity market position or the breadth and maturity of its broader portfolio.

Vendor Overview

Stellar Cyber provides a cross-layer security operations platform for enterprise SOCs, co-managed environments, MDR providers, and MSSPs. The product combines data collection, normalization, enrichment, native NDR, SIEM-like data handling, threat intelligence, case correlation, analyst workflow, AI-driven investigation, and response orchestration in a single operating environment.

Stellar Cyber is broader than a pure AI SOC point solution. Its historical Open XDR posture remains relevant to the architecture, but the current AI SOC fit comes from case-centered investigation, AI-driven triage, governed response, and auditability. Stellar Cyber’s customer base spans direct enterprise use and service-provider scale, including co-managed and fully outsourced delivery models.

Product and Architecture

The platform can be deployed as SaaS, on-premises, hybrid, or air-gapped, which makes it relevant for regulated, government, service-provider, and enterprise environments with different hosting requirements. The architecture begins with data collection through modular sensors, server sensors, log forwarding, API connectors, webhooks, and cloud storage inputs. Data is normalized, enriched with threat intelligence, placed into storage tiers, analyzed by detection pipelines, and correlated into cases.

The case is the primary work object. It carries evidence from intake through investigation, AI analysis, recommended actions, response workflow, and audit history. Stellar Cyber’s modular sensor is a key architectural element and can support functions such as deep packet inspection, IDS, malware analysis, vulnerability scanning, response actions, and log forwarding depending on configuration. The platform also integrates with endpoint, identity, vulnerability management, firewall, SaaS, cloud, and other sources.

Stellar Cyber does not provide a proprietary EDR and instead integrates with endpoint products such as CrowdStrike, SentinelOne, and Microsoft Defender. Action depth depends on configured connectors, permissions, deployment model, observables, and customer policy. The AI layer is multi-layered, using rules, machine learning, graph ML, LLM-supported case analysis, and task-specific AI agents. AI Case Analysis provides natural-language case summaries, while Alert Auto Triage / VSC performs deeper investigation of observables and produces verdicts with supporting findings.

Market Context and Positioning

Stellar Cyber receives an Emerging Player placement because its established Open XDR platform provides mature deployment, broad telemetry, cases, playbooks, and response reach, while its agentic decision layer and connector-level outcome verification remain early. Stellar Cyber occupies a position between SIEM/XDR, NDR, SOAR, and AI SOC categories. It is best evaluated as a broad security operations platform that includes AI SOC capabilities rather than as a narrowly scoped AI investigation assistant. The most relevant comparisons are SIEM platforms adding AI and automation, EDR/XDR platforms extending into cross-layer case management, SOAR products adding generative AI, MDR/MSSP platforms using AI to improve analyst capacity, and newer AI SOC point solutions.

Its differentiation is the combination of native NDR, SIEM-like data handling, graph correlation, AI triage, multi-tenancy, and case-to-action workflow in one platform. The service-provider route is important because MSSPs and MDR providers can use the platform to increase analyst capacity without linear headcount growth.

Narrative Implication

Stellar Cyber represents a platform-oriented approach to AI-driven security operations. The product starts with broad telemetry collection and correlation, then uses AI to summarize cases, investigate observables, explain entity relationships, recommend actions, and support analyst decisions.

The strongest publication framing is governed progression toward autonomy. Analysts validate AI verdicts, review findings and raw evidence, approve or execute actions, and feed overrides into institutional knowledge. Stellar Cyber’s strength is not narrow agent purity; it is the breadth of the operating platform and its ability to connect data, case, investigation, workflow, and service-provider delivery.

Radiant Security - Pioneer

Vendor Overview

Radiant Security is a pure-play AI SOC vendor focused on autonomous alert triage, AI-generated case intelligence, and human-governed response. Its strongest claim is broad alert coverage combined with accurate triage across both recurring and less familiar security signals. The platform is designed to handle common alert categories such as identity and email alongside less standardized signals from areas including WAF, DLP, IT, supply-chain, and external-threat sources.

Radiant differentiates through customer-specific Context Memory, generative triage that can construct investigation plans for unfamiliar alerts, persistent case management, integrated response actions, and native log management. These capabilities give the platform a broader operational role than an alert-summarization assistant, although the depth of response remains dependent on connected systems, permissions, and customer-defined authority.

Radiant’s current fit is strongest as a triage-to-case-to-governed-response platform. The product supports one-click actions and limited zero-click automation for approved scenarios. Where connected tools expose the necessary APIs and permissions, analysts can execute response actions directly from Radiant’s case environment. The platform records those actions in an audit trail, while reversibility depends on the underlying source system and action type. Broader context-aware autonomous response remains an area of product expansion rather than the default operating model today.

Product and Architecture

Radiant’s architecture has three main components: AI triage, integrated response actions, and log management. The triage pipeline classifies alerts, enriches them with endpoint, identity, threat-intelligence, and other relevant telemetry, constructs a triage plan, executes the required queries, and produces a malicious or benign conclusion. It can reuse plans for familiar alert types and generate new plans for unfamiliar signals.

The evidence model is a strength. Findings in the interface can be traced to the queries and evidence used to reach the conclusion, while cases serve as the durable operational work unit. Related alerts can be grouped automatically around shared users, devices, IP addresses, indicators, or other significant artifacts. The case view includes the summary, grouping anchors, verdict and confidence, recommended response actions, assignment, notes, and a full action history. Supported actions can be initiated directly from the case when the connected source system exposes the appropriate controls.

Integrations span endpoint, identity, email, cloud, network, SIEM, threat intelligence, ITSM, collaboration, custom API actions, and native log management. Response execution depends on active connectors, source-system APIs, and granted permissions. Radiant Log Manager can use customer-owned S3 buckets through a bring-your-own-bucket model, providing a lower-cost evidence store for AI-driven triage queries.

Radiant differs from traditional playbook-first SOAR by generating investigation plans and response recommendations from case context rather than requiring every scenario to be predefined. Execution remains governed by integrations, permissions, customer policy, and the authority assigned to the system. The platform can support one-click actions and limited zero-click automation, while higher-impact remediation depends on operational confidence and customer-defined controls.

Market Context and Positioning

Radiant Security receives a Pioneer placement because its architecture connects coherent investigation, case continuity, analyst feedback, and reversible one-click response, while zero-click governance and verified autonomous-response breadth remain developing. Radiant’s go-to-market is shaped around SOC insourcing rather than wholesale MSSP replacement. Many direct customers are organizations dissatisfied with previous MSSP or MDR outcomes and seeking greater context, broader alert coverage, and more consistent operations without adding headcount at the same rate. Radiant offers after-hours coverage as an add-on, but its primary positioning is to support an internally operated SOC rather than become a full-service 24/7 provider.

Radiant competes most naturally with AI SOC point solutions, MDR-modernization providers, and automation vendors adding AI. Its differentiators are alert-triage coverage, practical case management, evidence-linked AI reasoning, predictable pricing, integrated Log Manager capabilities, and a staged path from human-approved actions toward more automated response.

Narrative Implication

Radiant shows the AI SOC category moving from alert triage into case-level operational systems. Its strongest narrative is that AI can compress investigation, preserve evidence, group alerts into cases, and give customers a controlled way to determine how much response authority the system receives.

The product’s publication story is practical SOC insourcing. Radiant can help smaller teams increase investigative coverage and operational consistency without scaling analyst headcount at the same rate, while keeping response governed, visible, and bounded by customer-defined autonomy thresholds.

Intezer - Pioneer

Vendor Overview

Intezer is an AI SOC platform focused on autonomous alert triage, investigation, case management, detection engineering, and governed response across existing enterprise security stacks. The company has expanded from its roots in malware analysis, endpoint forensics, live-memory analysis, and reverse engineering into a broader SOC operating layer that works across SIEM, EDR, identity, cloud, email, network, and ITSM environments.

Intezer’s strongest differentiation is the combination of AI-led SOC automation with deep forensic analysis. Its genetic code and binary-similarity capabilities analyze assembly-level code fragments to identify relationships among unknown, modified, polymorphic, and previously observed malware. That evidence is combined with process trees, endpoint and network forensics, live-memory analysis, phishing analysis, threat intelligence, and MITRE ATT&CK mapping to support verdict generation and escalation.

The platform is best suited to enterprises and MDR or MSSP providers with established SIEM and EDR investments, significant alert volumes, and a need to expand SOC coverage without replacing their core detection estate. It is also relevant to organizations that value predictable licensing, evidence retention, regional data tenancy, expert escalation, and approval-gated response for higher-impact actions.

Product and Architecture

Intezer operates as a cross-layer SOC control plane that ingests alerts, gathers evidence, groups related activity into cases, performs forensic investigation, assigns verdicts, and recommends or initiates response actions according to customer policy. The platform can work with telemetry held in SIEMs, data lakes, or both, allowing customers to retain existing detection investments while adding a common investigation and response layer.

The forensic engine is central to the architecture. Intezer uses its proprietary genetic-code analysis alongside endpoint, network, process-tree, memory, phishing, and threat-intelligence evidence to support investigation. LLMs contribute to analysis, but Intezer describes a compartmentalized design in which model outputs are separated by triage component so a single anomalous response does not directly determine the final verdict.

The integration model includes standard connectors, custom APIs, customer-built extensions, and an MCP server that exposes the Intezer engine to external AI platforms, agents, and analyst interfaces. Customers can also build custom agents for reporting, threat hunting, and environment-specific workflows. Detection-engineering services extend the platform through posture reviews, MITRE ATT&CK gap analysis, ongoing rule assessment, and customer-owned detection content.

Intezer’s autonomy model centers on autonomous triage and investigation, with response authority governed by customer policy, action type, asset criticality, user privilege, and incident severity. The platform can support a spectrum from read-only recommendation through supervised execution, narrow automation, and broader automated response. Human escalation remains part of the operating model, supported by feedback mechanisms and access to forensic experts.

Market Context and Positioning

Intezer receives a Pioneer placement because its architecture connects deep forensic reasoning, cross-source investigation, and routine-case automation, while approvals, policy hierarchy, rollback, audit export, and broad outcome verification remain under-evidenced for Production Delivery. Intezer competes with AI SOC point solutions, MDR and MSSP automation platforms, AI-enabled SOAR vendors, and platform-native autonomy from SIEM, EDR, XDR, identity, and cloud-security providers. Its strongest positioning is where buyers value evidence-backed verdicts, low-severity alert coverage, malware and endpoint depth, and the ability to operate across heterogeneous security tooling.

Its commercial model is also a differentiator. Intezer describes pricing based on EDR license count rather than alert volume, tokens, agents, or consumption credits, with unlimited triage included. That model may be attractive to organizations with high alert volumes or those seeking to expand coverage gradually across multiple security domains without introducing usage-based constraints.

The strongest fit is an organization that already operates a SOC and has enough process maturity to define approval policies, automation thresholds, and escalation pathways. More mature teams may place greater value on MCP access, API extensibility, and customer-owned workflows, while resource-constrained organizations may rely more heavily on detection-engineering services, case grouping, and expert escalation. MDR and MSSP providers may benefit where multi-tenancy, alert scale, and consistent triage across customer environments are priorities.

Narrative Implication

Intezer reinforces the AI SOC market shift from alert summarization toward case-to-action continuity. Its category contribution is the combination of autonomous triage and investigation with forensic evidence designed to raise confidence before response actions are taken. This gives the platform a credible role as an operating layer across existing SOC infrastructure rather than as an isolated AI assistant.

The product appears broad across endpoint, malware, phishing, SIEM-connected alerts, detection engineering, case management, and customer-configured response. Buyers should validate the depth of required integrations, the approval model for high-impact identity and cloud actions, confirmation of response outcomes in source systems, rollback procedures, and the maturity of real-time organizational context in their environment.

SentinelOne - Emerging Player

SentinelOne is assessed in this profile only against the AI SOC capabilities available within its platform. Its placement does not reflect SentinelOne’s overall cybersecurity market position or the breadth and maturity of its broader portfolio.

Vendor Overview

SentinelOne is a platform-native security operations vendor with origins in endpoint protection and XDR. Its relevance to the AI SOC market comes from the Singularity Platform, which brings endpoint, cloud, identity, AI security, data, and security operations capabilities into a common operating layer. SentinelOne is not best understood as a pure-play AI SOC vendor. It is better understood as a large security platform attempting to make AI-assisted investigation, AI SIEM, automation, and response work from the same control plane.

The company’s AI SOC fit is strongest where the buyer already treats SentinelOne as a core security operations platform. In those environments, SentinelOne can connect investigation and response more directly than vendors that sit only above the stack. The platform has native control points in endpoint and adjacent identity, cloud, and workload security, and those control points give SentinelOne a credible path from detection and investigation into containment.

SentinelOne’s positioning reflects a broader market shift. AI SOC value is moving away from summarization alone and toward systems that assemble evidence, reason across security context, recommend or execute actions, and preserve proof of what happened. SentinelOne’s public platform messaging emphasizes a shared data foundation, one AI engine, one console, and real-time context across endpoint, AI SIEM, Purple AI, and Hyperautomation.

Product and Architecture

The AI SOC-relevant product set is centered on the Singularity Platform, Singularity AI SIEM, Purple AI, Hyperautomation, endpoint protection, cloud security, identity security, and emerging AI security capabilities. The architecture is platform-first. SentinelOne brings telemetry, detections, investigations, and response actions into a shared operating model rather than treating AI SOC as a separate assistant bolted onto an existing alert queue.

Purple AI is the most visible analyst-facing AI layer. It supports investigation, search, reasoning, and workflow acceleration across SentinelOne-native and integrated third-party security data. Singularity AI SIEM extends the platform into broader telemetry ingestion and analytics, while Hyperautomation provides the response and workflow layer across SentinelOne-native controls and third-party systems through integrations and APIs. Hyperautomation currently delivers broad deterministic workflow execution; more dynamic, reasoning-driven paths using LLM Actions and Dynamic Snippets remain in development.

SentinelOne’s control-plane advantage is also its architectural constraint. The product story is strongest when response actions run through native SentinelOne surfaces or well-integrated systems. In heterogeneous environments, the buyer still needs to understand which actions are native, which depend on third-party integrations, which require human approval, and how consistently the platform verifies closure after action. Those are normal deployment questions for any platform-native AI SOC approach, not signs that SentinelOne lacks relevance to the category.

SentinelOne has also expanded the platform through AI and data acquisitions. Prompt Security adds runtime visibility and protection for enterprise GenAI and agentic AI usage, including controls for sensitive data leakage and AI-specific threats. Observo AI, now offered as Singularity AI Data Pipelines, adds a native telemetry-management layer that filters, enriches, and normalizes security data before it enters AI SIEM or the Singularity Data Lake. These moves reinforce SentinelOne’s strategy to own more of the AI SOC stack: data intake, investigation, AI reasoning, response, and AI application security.

Market Context and Positioning

SentinelOne receives an Emerging Player placement because Singularity provides strong native response authority and proven production maturity, while unified incident cases, deeper reasoning-driven workflows, and end-to-end agentic continuity remain incomplete or on the roadmap. SentinelOne occupies the platform-native autonomy path in the AI SOC market. It competes less like a narrow autonomous investigation vendor and more like a security operations platform that can embed AI SOC capabilities across a large installed base. That gives the company a different buyer motion from pure-play AI SOC vendors. SentinelOne can appeal to teams that want to consolidate security operations around endpoint, XDR, AI SIEM, and automation rather than introduce a separate AI SOC control layer.

The strongest buyer fit is an enterprise that already relies on SentinelOne for endpoint or XDR and wants to expand into AI-assisted investigation, AI SIEM, and governed response without rebuilding the SOC architecture from scratch. For these buyers, SentinelOne’s value is continuity. The same platform that sees and controls a large part of the environment can increasingly assist with investigation and response. That can reduce handoffs, improve action speed, and make AI SOC adoption feel like an extension of current operations.

The fit requires a different evaluation for buyers committed to a mixed, best-of-breed stack. SentinelOne can ingest and normalize third-party telemetry, including through OCSF-aligned schemas, allowing Purple AI to investigate across a heterogeneous environment while Hyperautomation executes through integrated systems. Its differentiation remains anchored in the Singularity data and control foundation. For these buyers, the decision is not only about tool consolidation; it is also about how much telemetry, investigation context, and response workflow they are willing to consolidate onto SentinelOne’s platform.

SentinelOne also benefits from scale. The company has crossed the $1 billion annualized recurring revenue threshold, giving it a large customer base and commercial foundation for expanding AI SOC adoption. That scale matters because platform adoption, telemetry breadth, and customer trust shape how quickly autonomy can move from assisted investigation into governed response.

Narrative Implication

SentinelOne’s AI SOC narrative is that autonomous security operations will be easier to trust when AI is embedded inside the platform that already observes, investigates, and acts. The company does not need to argue that every SOC action should be handed to a standalone autonomous analyst. Its stronger argument is that response autonomy should grow from a security operations platform with native telemetry, native controls, AI-assisted investigation, and automation.

SentinelOne is one of the more credible platform-native candidates in the AI SOC market. Its strength is the ability to connect AI SOC capabilities to real control points, especially endpoint and related platform surfaces, while extending investigation and automation across third-party data and tools. Its constraint is that the current reasoning-to-action chain remains distributed across linked but separate records, and broader cross-stack autonomy depends on integration depth, governance design, and deployment maturity.

The implication for buyers is that SentinelOne supports more than a closed, native-only estate, but it does not operate as a neutral overlay. Its mixed-stack value comes from consolidating third-party telemetry and investigation context into Singularity, then extending response through Hyperautomation. The current platform is strongest where buyers want an increasingly converged data, investigation, and response foundation. A single unified incident case, deeper reasoning-driven workflows, and fuller agentic continuity remain important maturity markers rather than completed capabilities.

Qevlar - Pioneer

Vendor Overview

Qevlar AI is a pure-play AI SOC investigation and response platform for large enterprises and MSSP/MDR providers that want autonomous, repeatable case-building with governed containment. The company positions itself as an intelligence and investigation layer rather than a chat-first copilot, traditional SOAR clone, or narrow alert-triage assistant.

The product’s strongest claim is a deterministic, graph-based investigation engine that converts alerts into evidence graphs, explains how it reached a verdict, and then drives bounded actions across endpoint, identity, email, and other control planes. Qevlar’s buyer profile tends toward organizations with heterogeneous stacks, high alert volume, and a desire to amplify existing SIEM, XDR, and SOAR investments rather than replace them.

Product and Architecture

The product covered in this profile is Qevlar AI for SOC Teams. It is offered as an AI SOC investigation and response platform with packaged tiers spanning entry triage, L2-level investigation automation, advanced response, and proactive hunting. The platform autonomously investigates alerts, builds an evidence graph, reaches an explainable verdict, and recommends or executes next-step actions under policy. Deployment options include SaaS, bring-your-own-cloud on supported hyperscalers, and self-hosted or sovereign configurations for customers with data-residency and compliance requirements.

Qevlar’s architecture centers on a deterministic graph-based engine rather than an LLM-first copilot. Each investigation is modeled as a graph of observables, actions, evidence, and relationships. Qevlar combines deterministic tool and action selection with more adaptive reasoning for semantic interpretation, unfamiliar detections, and changing data models. LLMs can interpret context and synthesize findings, while executable tools and critical actions remain constrained by controlled workflows and customer policy. This boundary supports consistency, auditability, and resilience against prompt injection.

The platform is stack-agnostic and integrates across SIEM, EDR/XDR, identity, email and collaboration, ITSM, threat intelligence, SOAR, and custom APIs. A context module combines structured inputs such as VIP lists, trusted domains, and file hashes with unstructured organizational knowledge and context inferred through investigations and analyst feedback. Proposed context changes can be tested against historical investigations before they are promoted into production.

Response is governed and containment-oriented. Qevlar can move from investigation into response, with unattended automation best suited to low-risk or highly trusted use cases. Higher-impact containment remains better suited to approval-gated operation. Beyond individual alerts, Qevlar can correlate activity across sources and group related alerts into incident or campaign narratives spanning multiple users, timeframes, and security surfaces.

Market Context and Positioning

Qevlar receives a Pioneer placement because its graph-based reasoning, exposed evidence, persistent case construction, and bounded containment create strong Architectural Alignment, while consequential actions remain predominantly supervised and broader response delivery is still developing. Qevlar fits the AI SOC category as a detection and decision layer with response extensions. It spans autonomous investigation, multi-source evidence collection, case continuity, guardrailed action, and SOC performance insight. Its structured investigation history can also support detection-engineering recommendations, ingestion-gap analysis, and asset-risk context without positioning the platform as a full vulnerability or exposure-management system. Its go-to-market is both direct and MSSP-led, with a strong fit for providers operating across heterogeneous customer estates.

Competitive reference points include AI SOC point solutions, MDR modernization providers, SOAR and automation vendors adding AI, and platform-native autonomous response from endpoint, identity, SIEM, or cloud vendors. Qevlar’s differentiation is its graph/evidence architecture, traceability, governance claims, heterogeneous-stack fit, and intentional non-copilot posture.

Narrative Implication

Qevlar shows how the AI SOC category can move beyond recommendation text and chat interfaces into case-to-action continuity with strong governance. Its role is best understood as an autonomous security investigation and response system designed to turn alerts into evidence-backed decisions and bounded actions.

The clearest differentiator is persistent case construction. Qevlar preserves evidence, reconstructs activity across related alerts, applies context, and connects investigation to containment and targeted posture improvements. Its market relevance depends on whether customers want AI SOC as an independent investigation layer across heterogeneous security stacks rather than as a feature inside a single platform vendor’s control plane.

Dropzone AI - Pioneer

Vendor Overview

Dropzone is an AI-native SOC platform focused on agentic investigation, threat hunting, threat intelligence analysis, detection improvement, and governed response support. The product functions as an augmentation layer for security teams: human analysts retain responsibility for strategic direction and high-impact decisions, while specialized agents take on repeatable investigation, enrichment, hunting, and operational follow-through.

Dropzone’s strongest fit in the AI SOC market is the agentic SOC pattern. Multiple specialized agents work across SOC functions rather than limiting the product to alert summarization. The platform covers analyst, threat hunter, threat intelligence, detection engineering, incident response, and resilience workflows. Its customer posture spans direct enterprise use and MSSP/MDR environments.

Product and Architecture

Dropzone’s architecture is organized around specialized agents that operate across the existing security stack. Each agent replicates the techniques of expert human analysts. For example, the core AI SOC Analyst follows the OSCAR methodology: Obtain, Strategize, Collect, Analyze, and Report. Not bound by strict playbook rules, the system reasons through an investigation dynamically and adapts the investigation path as new evidence appears.

Transparency is central to the product architecture. The platform exposes the agent’s action graph, findings, raw API queries, SIEM queries, reasoning steps, and conclusions. This gives analysts a way to inspect how a conclusion was reached and turns the investigation record into a reviewable evidence trail.

Dropzone integrates with existing customer infrastructure rather than replacing the SIEM, EDR, ticketing, SOAR, or case-management layer. Customer-specific context can be ingested through APIs, entered manually, or learned from investigation feedback. The Context Graph and custom strategy features allow customers to encode environment-specific facts, exceptions, VIP accounts, known-safe configurations, and operational rules.

Response is governed rather than unrestricted. Dropzone supports response actions, notifications, and workflow-based response support, while high-impact remediation remains bounded by customer policy and analyst judgment. Native case-management demand is increasing, with many workflows continuing to rely on existing ticketing and case-management systems.

Market Context and Positioning

Dropzone AI receives a Pioneer placement because its multi-agent investigation model, evidence transparency, and contextual case progression align strongly with the AI SOC lifecycle, while unattended action breadth, risk policy, approval chains, retry behavior, and independent outcome verification remain developing. Dropzone sits in the AI SOC-native category, with particular strength around agentic investigation, evidence transparency, and role-based SOC augmentation. It is not a SIEM replacement, MDR provider, or traditional SOAR platform. Its market role is an agent layer that works across existing security investments and increases investigation, hunting, and response-preparation capacity.

Competitive comparisons include other AI SOC-first platforms, SOAR vendors adding generative AI, MDR/MSSP platforms using AI to increase analyst capacity, and SIEM/XDR vendors embedding AI into investigation workflows. Dropzone’s differentiation is its multi-agent operating model, visible investigation graph, OSCAR-based reasoning pattern, custom strategies, context memory, hunt-pack library, and support for SOC functions beyond alert triage.

Narrative Implication

Dropzone shows how the AI SOC category is moving from alert investigation into broader agent-led security operations work. The product’s strongest narrative is human-augmented agentic operations: agents take on structured investigation, evidence gathering, threat hunting, context application, and response support, while analysts retain judgment over trust, policy, and high-impact actions.

The result is a model of AI SOC that expands operational capacity without requiring buyers to replace the core security stack or surrender control over material response decisions.

D3 Security - Emerging Player

D3 Security is assessed in this profile only against its AI SOC capabilities available within its platform. Its placement does not reflect D3 Security’s overall cybersecurity market position or the breadth and maturity of its broader portfolio.

Vendor Overview

D3 Security is an established SOAR and automation platform vendor whose Morpheus platform combines AI-driven triage and investigation with native SOAR, case management, and governed response. Its go-to-market focus includes large enterprises and managed security service providers, with multi-tenant support and per-tenant controls supporting service-provider environments.

Morpheus gives D3 a SOAR-native path to AI SOC rather than operating as a standalone AI layer. The platform spans alert triage, investigation, decision support, response, case management, and audit on one system, while retaining a human-controlled and policy-governed response posture.

Product and Architecture

Morpheus is integrated into the D3 SOAR platform and runs on what D3 describes as a purpose-built Cybersecurity Triage Reasoning Graph. The architecture is designed to preserve security investigation logic independently of any single language model. Morpheus triages incoming alerts, gathers context, and assembles an incident workspace. Analysts can use natural-language adaptive tasking to launch broad or targeted investigations. Attack Path Discovery gathers evidence across connected security domains, maps affected entities and blast radius, and feeds a two-stage plan-and-build process that generates an incident-specific remediation playbook for analyst review before executable steps are created.

Morpheus exposes four configurable autonomy modes: Deterministic, AI-Assisted, AI-Led, and Autonomous. These modes can be set by workflow and tenant, allowing customers to vary approval requirements by action risk and operating environment. D3’s strongest current operating model remains deterministic or approval-gated execution. Broader autonomous operation is available on a more limited basis and should be evaluated by workflow, action type, and customer evidence.

The workflow begins with alert intake from SIEM, EDR, or other integrated tools. Morpheus performs triage and context gathering, collecting evidence, enrichment data, and relevant telemetry into an incident workspace with a case narrative, supporting artifacts, and recommended next steps. Analysts can inspect, edit, reject, pause, or approve the proposed plan. Approved actions execute through D3’s integrations across containment, identity, network, email, ticketing, and messaging where the customer has configured the required connectors, permissions, and approval policy. A durable incident case and unified audit trail preserve evidence, reasoning, approvals, integration calls, action outcomes, and closure history, although explicit policy-decision records and execution-identity attribution are less complete.

D3 highlights local knowledge injection as a differentiator. Morpheus can incorporate how a specific team handles incidents, including its tools, preferred steps, and established workflows, to align investigations and response plans with customer-specific practice. D3 also constrains agentic tasks through tool-scope limits, iteration caps, output validation, and approval gates.

Market Context and Positioning

D3 Security receives an Emerging Player placement because its SOAR foundation provides mature governance, integrations, case records, and auditability, while broad AI-led autonomous response and production proof for the newer Morpheus execution layer remain limited. D3’s primary positioning is a SOAR-native AI SOC platform that connects autonomous investigation to governed execution on a common case-management and audit foundation. The intended user is generally a Tier 2 or more senior analyst with enough experience to evaluate AI-suggested steps. Market fit is strongest for teams that value graduated autonomy, broad integration leverage, multi-tenant controls, and configurable oversight before consequential actions.

D3 competes with SOAR and automation platforms adding AI, AI SOC point solutions, and other vendors that connect investigation to response. Its differentiation is the combination of a model-independent reasoning architecture, native SOAR and case management, evidence-driven playbook generation, integration leverage, local knowledge injection, and multi-tenant support for service-provider environments.

Narrative Implication

D3 demonstrates a pragmatic, governed version of AI SOC. Many buyers are not ready to grant broad autonomous authority, especially for high-impact containment or identity actions. D3 addresses that constraint through graduated autonomy, evidence-driven playbook generation, and a shared case-to-action audit trail, while allowing customers to keep consequential execution approval-gated.

The strongest narrative is disciplined automation rather than unrestricted autonomy. The Reasoning Graph and Attack Path Discovery accelerate triage, investigation, and playbook construction, while D3’s SOAR, case-management, and integration foundation provides the governance and execution substrate for response at the buyer’s chosen autonomy level. The principal maturity question is how broadly customers are using the AI-Led and Autonomous modes beyond supervised, policy-gated workflows.

Critical Enablers: Upstream Architecture Behind AI SOC

The quality of automated response is bounded by the systems upstream of it. Endpoint, identity, cloud, network, email, application, and SaaS platforms determine what the AI SOC can see and what it can change. Endpoint isolation, token revocation, email quarantine, and cloud containment depend on reliable telemetry, safe write APIs, and correctly scoped credentials in the underlying tools.

Data fabric and ingest platforms determine whether the response system can reason across the environment from multiple signals. Collection, routing, normalization, enrichment, filtering, and cost-aware data movement affect the completeness and freshness of the case. Cleaner events and stronger entity context reduce avoidable uncertainty. Poor routing, inconsistent schemas, and missing history can make a sophisticated investigation engine reach the wrong conclusion quickly.

Storage, detection, and analytics platforms shape the evidence state before action begins. Correlation, entity resolution, anomaly detection, behavioral analysis, and case construction determine whether the response platform receives the right incident at the right confidence. These capabilities sit upstream of remediation authority and remain essential to response quality.

Control-plane design is equally important. Buyers should test whether integrations can distinguish read permissions from write permissions, enforce least privilege, report partial failure, support non-repetitive actions, and verify state after execution. AI SOC functions as the governing layer of an evidence and control chain, and the weakest dependency can limit the safety of the entire loop.

Vega - Post-SIEM Security Analytics Mesh

Vendor Overview

Vega is an upstream, AI-native detection, evidence, and security analytics vendor for AI SOC. Its center of gravity is not autonomous remediation in the narrow sense. The company is focused on the detection fabric and case-state foundation that makes higher-confidence action possible across fragmented security data. That distinction matters because automated response is not only a remediation market. It is also an evidence and detection quality market. Before a SOC can trust an AI system to recommend or execute a state-changing action, it needs a reliable way to detect relevant behavior, assemble the evidence, explain the case, and preserve the facts behind the decision.

Vega’s core product is the Security Analytics Mesh (SAM), a platform designed to federate analytics across heterogeneous security data sources without forcing all telemetry into one SIEM, data lake, or storage architecture. The platform is built for environments where relevant evidence lives across Splunk, Microsoft Sentinel, object storage, endpoint tools, identity systems, cloud logs, and other operational data stores. Vega abstracts that fragmentation so analysts, detection engineers, and AI agents can search, detect, correlate, and reason across data that would otherwise remain difficult to operationalize during an investigation.

This gives Vega a distinct role in AI SOC. It is best understood as a detection and decision-confidence layer. It helps the SOC determine what should be detected, whether the organization has the data required to detect it, what happened when a signal fires, why it matters, and whether the case is strong enough to act. In that sense, Vega supports the market’s move from alert handling to evidence-backed case work.

Product and Architecture

Vega’s architecture is anchored in federated security analytics and detection engineering. The platform connects to distributed telemetry sources and lets analysts query across them without needing to centralize every dataset first. This is especially relevant for modern SOCs because cost, data volume, schema variation, and retention strategy often leave important security evidence outside the primary SIEM. Vega’s premise is that the SOC should be able to use that evidence during detection, hunting, triage, and investigation even when it lives in different systems or storage tiers.

The detection engineering capability is a central part of the product. Vega is designed to let teams write detection logic once and apply it across relevant connected data sources. In the March demo, Vega demonstrated detections written against OCSF-normalized event types and fields rather than against a single vendor’s schema. A detection written for an EDR event type, for example, can apply across multiple EDR products when the connected tools report the required normalized fields. This allows detection logic to become more portable across tools and reduces the need to maintain separate versions of the same behavioral detection for each backend.

Vega also supports detection coverage and blind-spot analysis. The platform can show which detections apply to connected data sources, map coverage against techniques, and identify places where a technique is theoretically important but the organization lacks the right telemetry or active detection coverage. That shifts detection engineering from a content-library problem toward a detectability problem. The useful question is not only whether a detection exists, but whether the customer has the data, fields, and connected sources required to make that detection meaningful in its environment.

The platform’s natural language capabilities extend into both search and detection creation. Vega can translate analyst intent into KQL and distribute the query across connected environments. It can also use natural-language-to-KQL workflows to help generate detections, breaking down tactics and sub-techniques into candidate detection logic. This lowers the barrier for analysts who understand the behavior they want to find but do not want to hand-code each query variant across multiple backend systems.

Vega’s AI Triage Agent extends the platform from federated search and detection into autonomous investigation. The agent can generate investigative queries, correlate findings, build a timeline, identify relevant assets and observables, and produce a conclusion or verdict. The case output includes the data sources used, key findings, attack sequence, assets, IOCs, and enrichment context. This is the part of Vega that most directly aligns with the AI SOC thesis: the platform is not merely retrieving data or emitting alerts, it is turning detection output and fragmented evidence into a structured case state that can support downstream decisions.

Vega also has response adjacency. Actions such as blocking IPs or revoking identity sessions can be initiated from the same analytics framework, and customers can push recommended actions into existing SOAR platforms or other workflow systems. The product posture is hybrid rather than purely autonomous. High-impact or destructive actions remain subject to confirmation, while Vega’s stronger near-term role is to produce the detection, evidence package, and decision context that make those actions more defensible.

Market Context and Positioning

Vega sits upstream of many remediation-first AI SOC vendors. Its strongest alignment is with the part of the workflow where telemetry becomes detections, detections become case-ready evidence, and case-ready evidence becomes a decision that the SOC can defend. That makes it structurally different from vendors whose primary differentiation is governed execution, action approval, or closed-loop remediation. Vega is closer to the analytical substrate that those systems need before they can act responsibly.

This positioning is important because many SOCs do not fail at response only because they lack playbooks. They fail because their detection and evidence layers are fragmented. Detection logic is often tied to specific backends, telemetry lives in multiple storage locations, schemas drift, and analysts must reconstruct timelines across tools before they can decide whether an action is justified. In that environment, response automation can only go so far. A system that acts quickly on incomplete, non-portable, or poorly explained evidence increases operational risk. Vega’s value is that it improves the quality of the detection-to-case path before the action decision is made.

Vega’s cyber defense engineering story also connects to a broader market shift away from static detection-as-code as the end state. The next phase of detection engineering is not just writing better rules. It is building systems that understand what data exists, where it lives, which behaviors are detectable, how coverage changes across connected tools, and how detections should be promoted into investigation-ready cases. Vega’s federated model gives it a strong position in that shift because detection logic can be applied across the customer’s distributed data estate rather than being trapped inside one SIEM or one vendor’s control plane.

The buyer fit is strongest in mature, large enterprise, or data-fragmented SOCs where telemetry is distributed across multiple SIEMs, data lakes, object stores, endpoint platforms, cloud systems, and identity tools. These organizations often have enough data to detect and investigate more effectively, but they lack a practical way to access and operationalize it at investigation speed. Vega gives those teams a way to make more of their existing data estate useful without requiring full re-platforming into a single analytics backend.

Vega also fits organizations that want to improve AI SOC readiness before expanding autonomous action. The platform can support detection engineering, human-led investigation, AI-assisted triage, and downstream response recommendations while preserving human control over high-impact actions. That makes it relevant for enterprises that are not yet ready to grant broad autonomous response authority but still want to reduce the manual burden of detection development, evidence gathering, and case construction.

Narrative Implication

Vega shows that the AI SOC market should not be defined only by who executes the final remediation step. Action authority depends on detection authority and evidence authority. The vendors that earn the right to recommend or execute state-changing actions will need a reliable way to detect the right behaviors, assemble the right evidence, explain the case, and preserve case state across fragmented security data. Vega’s role is to make the SOC confident enough to act, even when the final action is executed through another control plane.

The company’s narrative strengthens the core AI SOC thesis by exposing a dependency that can otherwise be underweighted. Autonomous response is not trustworthy because a workflow can technically disable an account, block an IP, isolate a host, or create a ticket. It becomes trustworthy when the system can show why the signal mattered, which detection fired, what data supported the conclusion, what context changed the interpretation, and what uncertainty remains. Vega’s detection-first and evidence-first architecture aligns directly with that requirement.

Vega is therefore best positioned as a detection fabric and decision-confidence platform for AI SOC. It need not be the primary system of action in every customer environment, but it is a system of detection portability, investigation record, and case-state assembly. That role matters because the future SOC will need more than faster remediation. It will need higher-quality detections, stronger detectability awareness, better provenance, and a more durable link between fragmented telemetry and the actions taken in response.

Panther - Detection-as-Code and Security Data Foundation

Vendor Overview

Panther is a cloud native security operations platform centered on security data, detection-as-code, and AI-assisted detection engineering. Its relevance to AI SOC is primarily as an upstream architectural enabler: it improves the telemetry, detection logic, evidence quality, and investigation readiness on which reliable automated response depends. The platform combines centralized log ingestion, a customer-oriented data lakehouse, Python-based detection engineering, AI-assisted detection creation, AI triage, scheduled prompt-based hunting, and an emerging path toward agentic runbooks and approval workflows.

Panther has remediation capabilities through agentic runbooks, approval workflows, and response actions executed through connected tools. These capabilities allow the platform to move selected investigations into human-approved or policy-governed action. Its architectural center remains further upstream, where Panther collects and normalizes telemetry, operationalizes detection logic as code, supports recurring hunts, and exposes the evidence and reasoning behind alert triage. The maturity and breadth of response execution, rollback, approval-chain governance, source-state confirmation, case management, and broader autonomous remediation vary by release state and deployment scope.

Product and Architecture

Panther’s architecture centers on collecting security telemetry into a customer-oriented data lakehouse and applying detection logic, enrichment, AI triage, and response workflow on top of that evidence base. The platform can ingest from cloud and application security sources, Snowflake, Databricks, AWS Security Lake, and other log streams. Panther positions this architecture as a consolidation point for environments with disconnected SIEM, data, or log-management systems.

This architecture matters to automated response because weak telemetry, inconsistent schemas, and noisy or poorly maintained detections increase uncertainty before an action is selected. Panther’s role is to improve the evidence state that downstream analysts, agents, or response platforms use to determine whether an incident is actionable and what response is justified.

The AI Detection Builder lets users create or modify detections in natural language. The system generates Python detection code and presents a before-and-after view for human review, bridging plain-language analyst intent and Panther’s detection-as-code model. Panther also supports AI-assisted connector and schema creation for custom streaming sources, reducing some of the engineering burden associated with onboarding new telemetry.

AI Scheduled Prompts give customers a way to run recurring hunting questions over time. Self-tuning detection agents are intended to learn from false positives and suggest or apply detection changes. The AI Triage Agent investigates alerts, provides reasoning, follows runbook steps, and can recommend or initiate remediation through connected workflows while retaining human approval for sensitive actions. This gives Panther a credible path from detection and investigation into governed response, even though the product’s deepest capabilities remain in the data, detection, and evidence layers. Panther-hosted managed SaaS is available in supported US and EU regions, while bring-your-own data lake deployment provides more flexibility for customers with specific hosting needs.

Market Context and Positioning

Panther sits at the intersection of cloud native security data infrastructure, SIEM modernization, detection engineering, and AI-assisted SOC operations. Its primary market role is to provide the evidence and detection layer that supports investigation and downstream response, with agentic workflows extending the platform toward governed action.

Its competitive context includes cloud native SIEM and security data platforms, detection-engineering platforms, data-lake security analytics vendors, and broader SOC platforms adding AI-assisted investigation and response. Panther’s differentiation is the combination of a customer-oriented evidence layer, detection-as-code, AI-assisted detection creation, AI triage, open integration, MCP support, and engineering-native workflows.

The strongest buyer fit is a cloud-oriented or technology-forward SOC with substantial log sources and enough security engineering maturity to manage detections, schemas, data pipelines, approvals, and policy boundaries. Panther can support remediation through connected runbooks, approval workflows, and response actions, while its primary differentiation remains the programmable and inspectable evidence foundation that improves the quality of downstream investigation and action.

Narrative Implication

Panther shows that AI SOC readiness begins before investigation and remediation. Reliable action depends on whether the SOC can collect the right telemetry, normalize custom sources, maintain high-quality detections, preserve evidence, and continuously improve the signals that initiate the response loop. Panther’s architectural contribution is to make that upstream evidence and detection layer more programmable, inspectable, and adaptable.

The product’s publication story is a disciplined evolution from detection-as-code into AI-assisted security operations. Panther is strongest where the buyer values customer-controlled security data, detection quality, engineering-native workflows, and analyst-supervised automation. Its remediation capabilities extend that architecture into governed action, while its central contribution to AI SOC remains the evidence and detection foundation on which trustworthy response depends.

The Managed AI SOC: Service-Led Security Operations

The AI SOC market is developing through both customer-operated platforms and managed service models. Some organizations want to own the architecture and workflow, while others need the operational outcomes without the staff or maturity to achieve them alone.

TSRO is independent of whether the SOC is operated internally, through a managed service, or in a hybrid model. In many cases a managed AI SOC can execute portions of the response process, but the customer still defines which authorities are delegated, who is accountable, evidence standards, and outcome verification. The Trusted SOC describes the governance and reliability of action, not the sourcing model used to staff it.

Modern MDR providers can fill this role by combining AI-enabled investigation and response platforms with a human service layer. The provider operates the workflow from detection to response on the customer’s behalf. MDR providers should not be ranked directly against a software platform because the customer is purchasing a different operating model. A platform is evaluated by what the customer can configure, govern, and operate. An MDR provider is evaluated by the quality, speed, and transparency of the outcomes they deliver.

The service-led model is particularly relevant for cloud native companies without mature internal security operations, and organizations with persistent staffing constraints. Its effectiveness still depends on integration depth to customer systems, data and telemetry access, clear permissions, transparency, and the provider’s ability to adapt at scale across multiple tenant environments.

Daylight Security - Service-Led AI SOC

Vendor Overview

Daylight Security is a managed agentic security services provider that offers MDR, continuous threat hunting, and security data lake services with an AI-native investigation and response platform. The company is best understood as a service-led AI SOC operating model rather than a standalone software product for customer self-operation.

Daylight delivers managed agentic security services for organizations that need security operations outcomes without building or staffing a full internal SOC. The platform handles alert investigation, telemetry-driven detection, enrichment, verdict generation, response support, and case communication. Daylight’s security experts build and tune integrations, detections, and context repositories, and step in where cases require expert involvement.

Product and Architecture

Daylight’s architecture has three connected elements: a managed services layer, the investigation and response platform, and a customer-context/data layer. The platform can ingest alerts from third-party tools and can also operate on raw telemetry using Daylight-authored detections. Customers are not required to operate a SIEM for Daylight to investigate events, although the service can integrate with existing security tools.

The Daylight data lake functions as an investigation context repository. Standard MDR customers receive a defined retention window for investigation context, while longer retention and searchable historical data are provided through the separate agentic security data lake service. The platform is cloud-oriented, with regional isolation options for customers operating in different jurisdictions.

Daylight’s integration model is open and service-assisted. The platform can collect identity, endpoint, network, SASE/VPN, cloud, and organizational context to build a timeline and reach a verdict. Custom integration work is part of the service model, which allows Daylight to adapt investigations to customer-specific environments.

Response is policy-dependent. Non-destructive and lower-friction actions can be executed where customer policy allows, while higher-impact actions generally require customer permission or customer execution. The platform preserves case evidence, user verification, chat transcripts, Slack or Teams discussion context, and response/remediation audit information.

Market Context and Positioning

Daylight occupies the managed-service branch of the AI SOC market. It is not a like-for-like substitute for a customer-operated AI SOC platform where the buyer intends to own every operating step internally. Its most direct competitive frame is legacy MDR and MSSP providers, especially for customers seeking more tailored cloud coverage, AI-assisted investigation, and higher service quality.

The company fits two primary buyer groups: AI-native or high-growth cloud native organizations that need coverage without building an internal SOC, and mid-enterprise organizations that already use MDR but want better customization, speed, and outcome quality. This makes Daylight most relevant where the buyer wants an outcome-oriented security operations service with a platform-enabled investigation and response layer.

Narrative Implication

Daylight shows that the AI SOC market is not only developing as software for internal SOC teams. A credible branch of the category is emerging around service-led agentic security operations, where the AI platform and human service layer are designed together.

The completeness of Daylight’s model is service-led. Its value depends on the combined platform-plus-service design, customer-granted integrations and permissions, Daylight-operated detection and context work, and jointly agreed response policies. That makes Daylight an important example of AI SOC as an outcome delivery model, not just a software architecture.

Conclusion

Building the Trusted SOC established that AI SOC maturity is determined at the level of individual response actions through the Trusted Security Response Operations model. This market analysis applies that operating model to the products buyers can evaluate today. It shows where vendors can support the Trusted SOC, how reliably they can deliver capability in production, and which operating environments strengthen their fit.

The two ranking dimensions reflect that connection. Architectural Alignment measures how completely a product preserves the trust model from evidence through verified outcome. Production Delivery measures whether the product has the integrations, governance, and controls to sustain the operation model in practice. Together they indicate how well each vendor can support an earned response authority.

The rankings also show why a Trusted SOC will take different forms across organizations. AI-native vendors can strengthen a heterogeneous environment, while SOAR-derived vendors can provide a mature deterministic foundation to a regulated environment. Broader security platforms can connect native telemetry and enforcement with a lower integration friction. Critical upstream enablers improve evidence and detection foundations required for trustworthy decisions, while managed providers can operate portions of this lifecycle for customers that need outcomes instead of self-management. Each path supplies different parts of the Trusted SOC and places different responsibilities on the customer.

The authority portfolio should guide vendor selection. Buyers should define which actions software may recommend, prepare, execute, or verify, then use this analysis and their own diligence to select the architecture and delivery model best suited to those responsibilities in their environment. A Trusted SOC develops as the organization expands that authority only when evidence, controlled execution, and verified outcomes support it.

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SACR is introducing its first report on Digital Risk Protection. The wording around External Trust Operations as the term for this emerging operating model marks the point where Digital Risk Protection moves from artifact-level detection to campaign-level accountability.

Digital Risk Protection (DRP) programs were built to find visible abuse of known brands, domains, executives, products, and customer-facing assets. That mission remains necessary, but the external threat has outgrown an alert-and-takedown operating model. One campaign can now move across social accounts, cloned sites, fraudulent ads, messaging channels, synthetic personas, mobile apps, and payment or credential-harvesting flows. Each artifact may look like a separate case even when the same adversarial network connects them. AI increases the pace by making convincing content, localization, variation, and relaunches cheaper and easier to produce.

The category is resetting around a broader operating requirement: external trust operations, where the protected object expands beyond a domain, account, or listing to the business entity and the public trust relationships being exploited. The campaign becomes the unit of analysis, and the work from discovery and assessment through response, verification, and recurrence monitoring becomes one connected workflow measured by verified campaign closure.

Research Basis

This analysis combines current public threat reporting and vendor positioning with SACR’s ongoing research across digital risk protection, brand protection, threat intelligence, external attack surface management, fraud, narrative intelligence, and executive protection. Public sources establish the threat environment, SACR’s analysis defines the category and buyer implications, and Outtake examples show how one vendor is approaching the operating problem.

The accompanying market map is a convergence map built from vendors’ primary market motions and functional relevance to external trust operations. Placement identifies a contribution to the workflow without implying validation, parity, endorsement, or leadership.

Classic DRP capabilities still matter, but the buyer’s question is whether the program can progress from isolated artifact detection to coordinated, evidence-backed closure. The lasting requirement is an operating model that connects external abuse to business context, brings the right internal owners into a shared response, and proves that the active threat pathway was closed.

CISOs should evaluate whether a platform and program can:

1. Model the business entities and trust relationships that require protection, including brands, executives, employees, products, locations, subsidiaries, partners, domains, apps, official accounts, and customer-facing services.

2. Discover abuse beyond static watchlists, explain why a signal matters, and connect related accounts, content, infrastructure, channels, and monetization paths into a campaign.

3. Route evidence and response across security, fraud, legal, communications, brand, customer support, and executive protection without forcing each team to reconstruct the case.

4. Measure verified campaign closure, recurrence, unresolved infrastructure, analyst effort, and business harm. A completed takedown request is one response action, not proof that the threat has ended.

Outtake sponsored this research and appears later as an illustrative product profile. SACR’s category definition and buyer framework apply across the market. Buyers should still validate production performance, integration depth, remediation success, recurrence reduction, and business impact rather than treating product direction as proof of outcomes.

Why DRP Is Resetting

DRP became valuable because organizations lost control over how their brands, domains, executives, products, and customer-facing services appeared across the public internet. Attackers could register confusing domains, publish phishing pages, impersonate executives, clone applications, and misuse marketplaces faster than most teams could monitor manually. By bringing those assets into a repeatable monitoring, investigation, escalation, and takedown workflow, DRP gave enterprises a practical response to a rapidly expanding problem.

That mandate remains necessary, but a visible artifact rarely represents the full case. A fake executive account may lead to a cloned support page, moving victims into a messaging channel where a synthetic persona continues the conversation. It may also share infrastructure with fraudulent advertisements, lookalike domains, credential-harvesting pages, or payment accounts. What appears to be a collection of unrelated alerts can often be one coordinated operation, allowing a watchlist to find the entry point without revealing the full path to harm.

The Public Enterprise Is an Attack Surface

Security architecture usually begins with the internal enterprise: identities, endpoints, cloud services, SaaS applications, networks, and data. The organization’s public identity is different because it is distributed across platforms and intermediaries the enterprise does not control, including registrars, app stores, advertising networks, marketplaces, search engines, messaging services, review sites, and social networks.

The organization knows which identities, accounts, domains, applications, and support channels are legitimate, but customers, employees, investors, partners, and automated systems must often infer legitimacy from what they encounter. That asymmetry creates an external trust gap that attackers exploit with impersonation, synthetic media, manipulated narratives, and fraudulent infrastructure.

Enterprise AI agents now face the same problem as they retrieve web content, process email and documents, summarize public sources, and make recommendations from external information. Manipulated pages, fake entities, poisoned documents, and adversarial instructions can enter internal workflows through systems that trust public content. This does not turn DRP into a complete AI security category, but it raises the importance of external legitimacy as an input to automated decisions.

AI Changes the Economics and Tempo

AI changes the economics of deception by letting adversaries produce more convincing material, vary it across channels, and localize it for specific targets at far lower cost. Phishing copy, profile images, cloned voices, translated scripts, altered imagery, and fraudulent advertisements can be tested and revised quickly, increasing the pressure on manual review.

Current threat reporting reinforces this shift. The UK’s National Cyber Security Centre assessment through 2027 concludes that AI will continue to improve reconnaissance, vulnerability research, social engineering, malware development, and the processing of stolen data. Verizon’s 2026 Data Breach Investigations Report found that generative AI bolstered 15 percent of observed attack techniques and reported higher click rates for mobile phishing.

Together, these findings point to an acceleration of familiar attack methods rather than a separate class of external threat. Abuse will arrive in more forms and move across channels more quickly, leaving programs built around static lists and isolated alerts to validate fragments while the campaign continues elsewhere.

Crossing Ownership Boundaries

External trust threats cross organizational boundaries because each team encounters a different expression of the same campaign. Security may discover a fake domain while fraud sees customer losses; brand and legal teams may focus on an impersonating account and its takedown; communications, customer support, and executive protection may see reputational damage, victim reports, or a synthetic persona. Each signal creates a legitimate local task, but the campaign becomes visible only when those perspectives are connected.

This fragmented ownership allows for campaigns to persist through multiple remediation actions. Removing a malicious domain does not automatically trigger a takedown of the social account directing traffic to it. A shared case model can connect the entity under attack, related artifacts and infrastructure, required evidence, actions already taken, and the conditions that must be met before closure to the fragmented teams that are responsible for each individual step of the process.

Defining External Trust Operations

External Trust Operations is the operating model emerging within the DRP category reset. It connects business entities, campaign analysis, coordinated response, and verified closure so that external abuse can be managed as an operating problem rather than a collection of unrelated alerts.

The model gives DRP a clearer purpose: protect the public trust surface surrounding the organization’s brands, people, products, locations, subsidiaries, partners, customer-facing services, domains, apps, accounts, and narratives, while understanding how adversaries exploit the relationships between those entities and the people or systems that rely on them.

Protected Entities Give Context

Traditional DRP programs commonly begin with known assets such as brands, domains, executives, keywords, trademarks, and logos. A protected-entity model adds the operational context required to determine what is legitimate, why a signal matters, and how apparently different artifacts may relate to the same target.

• Products combine official names and imagery with SKUs, authorized sellers, marketplaces, regional availability, and known counterfeit patterns.

• Executives combine aliases, verified accounts, public appearances, imagery, organizational relationships, and common impersonation scenarios.

• Locations combine signage, geocoordinates, local listings, regional languages, and customer communications.

This context helps a platform judge relevance and connect fragments that use different text, imagery, or infrastructure. A suspicious profile, cloned login page, advertisement, messaging channel, and payment destination may first become a coherent campaign through their relationship to the same executive, product, customer workflow, or brand.

Campaign as the Unit of Analysis

Artifact-level response remains necessary because teams still need to report accounts, remove pages, block domains, preserve screenshots, and escalate listings. The campaign view provides the context needed to determine whether those individual actions address the full threat or only one visible part of it.

By connecting domains, accounts, personas, and other artifacts, campaign analysis reveals behavior that individual nodes cannot show on their own. A low-reach account may justify monitoring in isolation, yet the same account can require an urgent cross-functional response when it is tied to a cloned support page, active advertising, victim reports, and a payment channel.

The Operating Sequence

The operating sequence connects six activities that are often split across tools and teams. Each stage should preserve enough context and evidence for the next stage to act without reconstructing the case.

INTERPOL’s Spotlight Cybercrime analysis illustrates the connection stage in practice. Its Project Rapid example shows investigators starting with a single suspicious URL and identifying related phishing sites through lookalike visuals, shared hosting, and similar creation dates. The example shows why discovery becomes more valuable when it expands an artifact into an evidence-backed campaign view.

Verified Campaign Closure

Takedown is one of several remediation actions, but isn’t a good measure of a campaign being closed effectively. A case reaches verified closure when the organization has enough evidence to conclude that the active threat pathway has been disrupted, related assets have been addressed or consciously accepted, the right owners have completed their actions, and recurrence monitoring is in place.

Closure must reflect the residual risk of each campaign. An account can disappear while its supporting domain network remains active, a phishing page can be removed after credentials have already been collected, and a counterfeit listing can return through another seller, so the closure standard must account for what remains capable of causing harm.

Performance should be measured across the full campaign lifecycle rather than through alert volume or takedown throughput alone:

• Discovery and analysis: time to discover unknown abuse, determine relevance and severity, and connect related artifacts into a campaign.

• Coordination and response: cross-functional handoff time, analyst effort per case, and time to remediation.

• Outcomes and residual risk: percentage of cases with verified closure, recurrence after action, unresolved campaign infrastructure, and customer, employee, executive, or brand harm.

Together, these measures show where the operating model breaks. Fast takedown can coexist with weak campaign understanding, high alert volume can conceal poor prioritization, and a low backlog can reflect cases that were closed before the threat was resolved.

Market Convergence and Category Boundaries

The next generation of DRP draws on markets that developed around different control points:

• External monitoring and removal: classic DRP and brand protection contribute monitoring, investigation, takedown, trademark protection, counterfeit detection, and marketplace response.

• Adversary and infrastructure context: threat intelligence, open-source intelligence, and external attack surface management add underground-community, campaign, infrastructure, and unmanaged-asset context.

• Harm, trust, and people-centered response: fraud, trust and safety, narrative intelligence, and executive protection contribute monetization, synthetic-media, reputational, impersonation, and customer-abuse perspectives.

These categories overlap because an external campaign can move through several control points without fitting cleanly into any one of them. A platform may detect the infrastructure, another may understand the fraud path, and a third may own the relationship needed for removal or customer protection. Their value depends on whether the organization can connect those perspectives into one case.

The market is therefore unlikely to collapse into a single product class. Organizations will continue to use security operations, threat intelligence, fraud, legal, communications, brand protection, and customer-support systems, while DRP vendors compete to become the connective layer around the external entity and campaign. That layer should assemble context, preserve evidence, coordinate action, and maintain an outcome record across the systems teams already use.

Vendors may appear in multiple containers because this is a convergence market. The important point is not to force every vendor into a single box; it is to show how different control points are converging around the same external trust problem.

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How DRP Risks Show in Reality

Executive Impersonation and Fraud

Consider an executive impersonation campaign that develops into a customer fraud funnel. The organization first encounters a social account using a senior executive’s name and image, but the account is only the entry point: it directs users to a cloned customer-support page, moves the conversation into Telegram or WhatsApp, and ultimately sends victims toward a credential-harvesting or payment flow.

A conventional workflow may find the first account through a watchlist, keyword match, or customer report, confirm the impersonation, and submit a takedown through security, legal, or brand protection. Closing the case when that account is reported or removed leaves the rest of the campaign free to continue through related profiles, fraudulent advertisements, messaging channels, cloned pages, redirects, and payment infrastructure. The adversary can also relaunch under a different name or shift victims to another platform.

An external trust operations workflow begins with the executive and the connected customer journey. Authorized accounts, official domains, known imagery, login flows, brand relationships, and public context establish what legitimate activity looks like; profile similarity, linked domains, forms, calls to action, infrastructure, and customer reports help determine whether a new signal belongs to the threat.

The investigation then expands from the first account into a campaign view that connects profiles, advertisements, domains, sites, messaging channels, infrastructure, personas, and monetization paths while showing which relationships are supported by evidence. The organization can prioritize the campaign by reach, velocity, customer and credential risk, fraud exposure, executive impact, and recurrence, then route a shared evidence base to the teams responsible for technical response, fraud intervention, platform action, audience warning, and executive protection.

Closure requires evidence that the organization addressed more than the initial account. The case remains active until related assets have been assessed, the fraud path has been contained or consciously accepted, continued victim reports have been checked, and recurrence monitoring is in place.

Outtake.ai

Outtake is a useful proof point for the shift toward entity-centric external trust defense. Its platform models protected entities, uses specialized agents to interpret multimodal external content, applies signal-based triage, maps related artifacts through threat graphs, coordinates platform-specific takedowns, and preserves context for response and recurrence analysis. The company is best understood as an illustrative solution profile rather than the sole definition of the category.

Why Outtake Fits the Thesis

Outtake organizes protection around brands, people, locations, and products, enriching each entity with relevant context such as authorized accounts, imagery, aliases, geocoordinates, and product information. This gives the platform richer context than static keyword lists alone.

This maps to the report’s broader argument that the protected object is the organization’s public trust surface, which Outtake describes as its Digital Trust Footprint. Entity context helps establish what is being impersonated, how the activity connects to a broader campaign, and who should act.

Search agents use entity descriptions, visual context, transcripts, text overlays, authorized social accounts, and web research to determine relevance. In one example, context-based filtering narrowed roughly 3,000 collected items to 389 relevant findings, illustrating how entity-aware reasoning reduces broad collection to an actionable set.

Agentic Search and Multimodal Analysis

Outtake’s search layer looks beyond pre-approved watchlists and keyword strings across traditional web domains, social media, deep web, dark web, and customer-reported mobile threats such as smishing. Specialized agents interpret text, images, video transcription, logos, visual similarity, and indirect references before connecting findings to protected entities. Content Wall then aggregates agent-found material into a controlled view, allowing analysts to review external content without visiting each source platform directly.

Intelligent Workflows and Remediation

Outtake turns raw findings into explainable triage and response paths using signals such as external links, Telegram references, profile-image similarity, login forms, MX records, credential-harvesting JavaScript, and infrastructure clues. These signals inform risk scoring and help route related artifacts to the appropriate owners while preserving them within a common case.

Flagged queues and takedown workflows support graduated automation, moving from manual review toward automated enforcement as the buyer gains confidence in precision. Buyers should still validate production performance, approval requirements, and how closure is verified after action.

ReconAgent and Campaign Graphing

ReconAgent is the clearest product example of the shift from artifact monitoring to adversarial network understanding. In one example, an investigation began with a single suspicious social media account and expanded into a graph of 27 nodes and 31 edges across various social accounts, communication channels and servers, as well as a dedicated website and live app.

The significance is the operating pattern: a single suspicious artifact becomes the starting point for mapping related accounts, communities, infrastructure, and monetization paths. This moves investigation beyond isolated takedowns and gives analysts a stronger basis for prioritization, evidence packaging, disruption, and recurrence monitoring.

Adaptive Learning and Persistent Trust Context

Analysts can label findings, edit learnings, and feed those decisions into future triage. An observer-agent concept can also recommend backfill when new learning would alter the classification of existing alerts. Outtake’s Digital Reservoir preserves the resulting entity context, suspicious variants, fraudulent assets, adversarial infrastructure, evidence, decisions, takedown outcomes, recurrence signals, and prior cases. Together, these capabilities support adaptive case quality and persistent external trust context.

Digital Trust Kill Chain, Provenance, and Intake

Outtake describes its Digital Trust Kill Chain as extending the MITRE ATT&CK approach beyond traditional cyberattacks to map eight stages across four zones:

1. Zone 1: Discovery

   1. Reconnaissance: The attacker profiles an organization’s public attack surface before building the campaign.

   2. Infrastructure Setup: The attacker creates and stages the assets needed to support the operation, including fake domains, applications, and accounts.

2. Zone 2: Exposure

   1. Trust Exploitation: The staged infrastructure is made credible through brand impersonation, fake endorsements, and fraudulent use of trademarks, logos, and other recognizable signals.

   2. Target Engagement: The attacker initiates targeted contact with the intended victims through phishing, social media, messaging platforms, and malicious advertising.

3. Zone 3: Compromise

   1. Engagement Capture: The initial engagement becomes a controlled interaction between the victim and the staged malicious infrastructure.

   2. Value Transfer: The attacker leverages the controlled interaction to gain value through direct financial transaction, account takeover or compromise, or data exfiltration.

4. Zone 4: Harm

   1. Impact and Fraud: The attacker’s operation produces a business or human impact, including consumer scams, reputation damage, data exposure, or risk to physical operations.

   2. Monetization: The attacker converts and launders the operational proceeds into financial return.

The analytical value that the Digital Trust Kill Chain brings is that it connects what can initially appear as separate artifacts into a progressing operation. Outtake also emphasizes that intervention becomes more expensive as the campaign moves from the Discovery and Exposure zones to Compromise and Harm. Buyers should treat Outtake’s Digital Trust Kill Chain as an effective vendor-developed model for evaluating campaign maturity and response coverage.

Outtake’s Digital Trust Kill Chain framework, modeled after the MITRE ATT&CK framework, includes stages such as reconnaissance, infrastructure setup, trust exploitation, target engagement, credential capture, account takeover, impact and fraud, and monetization. This is useful as a vendor-developed framework because it shows how external trust attacks can be understood as multi-stage campaigns rather than isolated artifacts. Buyers should treat it as one lens for evaluating campaign maturity, not as the only methodology for the category.

Actor Tracing connects related activity, infrastructure reuse, and other actor context to support executive protection, adversary dossiers, remediation, and recurrence monitoring. Buyers should distinguish this form of provenance and adversarial-network analysis from definitive attribution, which remains difficult.

Outtake Intake structures customer- or user-reported scams, impersonation, smishing, and suspicious external activity into intelligence, alerts, and takedown workflows. This gives organizations another early-signal path for detecting and responding to external trust abuse.

Integrations and actionability

Outtake uses an open API model to move digital trust context into systems such as Microsoft Sentinel, ServiceNow, Jira, and OpenCTI. This supports SOC escalation, threat intelligence, workflow management, legal review, fraud investigation, and operational response without creating another disconnected queue. Buyers should distinguish customer-deployed integrations from directional or roadmap support.

Analytical assessment

Outtake is compelling because it aligns with the category’s most important shifts: from keywords to entities, from single alerts to threat graphs, from monitoring to investigation, from takedown to remediation workflows, from static configuration to adaptive context, and from brand-only protection to broader external trust defense.

Outtake’s category-shaping terms are useful, but the category should not become dependent on one vendor’s language. Evidence is the second risk. Product examples show capability direction, while claims about reduced triage time, takedown success, recurrence reduction, or business harm reduction still require customer evidence, anonymized case studies, or measurable before-and-after data. Scope is the third risk. Outtake’s model touches DRP, threat intelligence, executive protection, fraud, brand protection, narrative intelligence, and digital trust. That breadth is strategically interesting, but buyers should not assume that one platform replaces every adjacent category. The stronger framing is that Next-Gen DRP complements existing tools by connecting external trust signals around business entities and response workflows.

Buyer Scenario: Executive Impersonation Fraud Funnel

A practical buyer scenario helps make the category shift concrete. Imagine an executive-impersonation campaign that turns into a customer fraud funnel. The organization first sees a suspicious social account using a senior executive’s name and image. The account links to a cloned customer-support page, pushes users toward a Telegram or WhatsApp channel, and redirects victims into a credential-harvesting or payment-redirection flow.

A traditional DRP workflow may find the visible artifact through a watchlist, customer report, or keyword match. An analyst validates that the account, page, or domain abuses the brand or executive identity. Legal, brand protection, or security submits a takedown request. The case may be treated as closed when the artifact is removed or reported. The problem is that related social accounts, messaging channels, fraudulent ads, redirect paths, cloned pages, and monetization infrastructure may remain active or reappear later.

A Next-Gen DRP workflow starts with the protected entity. The platform understands the executive, the associated brand, the customer login flow, authorized accounts, domains, products, and known public context. It can detect suspicious social profiles, domains, images, videos, ads, or messaging channels even when the attacker avoids exact keywords. It uses signals such as profile-image similarity, external links, login forms, Telegram or WhatsApp calls to action, infrastructure reuse, and content similarity to assess relevance and severity.

The platform then connects the campaign. It links the fake profile, cloned page, messaging channel, domain, ad, infrastructure, and monetization path into a single case or graph. It prioritizes the case against customer harm, credential-harvesting risk, fraud exposure, executive exposure, reach, velocity, and recurrence. It routes evidence to security, fraud, legal, communications, executive protection, customer support, and external platforms based on each team’s role. It verifies whether visible artifacts were removed or contained, whether related infrastructure remains active, and whether recurrence appears after takedown. It also learns from the case by using analyst feedback, takedown outcomes, recurrence patterns, and entity context to improve future triage.

Buyer Action Plan: First 90 Days

A useful evaluation begins with the organization’s external trust problem, tests vendors against realistic campaigns, and moves a limited set of high-value use cases into production. The first 90 days should build evidence progressively rather than treating a feature demonstration as proof of operating performance.

First 30 Days: Define the External Trust Baseline

The first month should establish what the organization is protecting, how work moves today, and which measures will show whether the program improves.

• Define the highest-priority entities and the context required to protect them, including aliases, imagery, official accounts, domains, geographies, business relationships, customer journeys, and common abuse patterns.

• Map discovery, investigation, escalation, takedown, verification, and recurrence monitoring across security, fraud, legal, brand, communications, customer support, and executive protection. Identify where cases stall, split into duplicate work, or close without verification.

• Identify the systems that need external trust context and establish baseline measures for alert volume, analyst effort, severity decisions, campaign connection, handoff time, remediation, recurrence, and premature closure.

Within 60 Days: Test Discovery, Reasoning, and Campaign Connection

The second month should test whether the platform can move from isolated signals to an explainable campaign view under realistic operating conditions.

• Run current or representative scenarios in which adversaries avoid exact keywords, rely on images or video, use multilingual content, change platforms, or separate the initial lure from the eventual fraud or credential path.

• Require the platform to explain relevance and campaign connections through inspectable entity context, content, infrastructure, relationships, and evidence. The campaign view should distinguish supported relationships from weak or inferred links.

• Confirm that evidence, analyst decisions, indicators, screenshots, infrastructure details, and action history can move into each owner’s systems. Validate deployed integrations, remediation approvals, and the boundary between observable actor context and unsupported attribution.

Within 90 Days: Operationalize Remediation and Verified Closure

The final month should place a bounded workflow into production and test whether the organization can prove that its actions changed the active threat.

• Launch a limited production workflow around a small set of high-priority entities and abuse scenarios, with clear response ownership, evidence requirements, approval gates, and closure conditions.

• Measure discovery, severity decisions, campaign connection, handoff time, analyst effort, remediation, recurrence, unresolved infrastructure, and verified closure against the baseline established in the first month.

• Reopen apparently closed cases to check for surviving assets, platform migration, continued victim reports, or new infrastructure. Use the results to improve entity context, workflow rules, recurrence monitoring, and recurring governance.

Market Implications

Source coverage will remain part of DRP buying decisions, but it will carry less weight on its own as campaigns become more connected and operationally complex. Buyers will increasingly assess whether a platform can model protected entities, discover unknown abuse, interpret multimodal content, connect campaigns, explain prioritization, preserve evidence, coordinate workflows, support remediation, and monitor recurrence.

Competitive pressure will come from several vendor lineages. Established DRP and brand-protection providers bring collection breadth, takedown experience, and mature customer workflows; threat-intelligence and external-attack-surface vendors contribute infrastructure and adversary context; fraud and trust-and-safety platforms understand customer abuse and monetization; and newer AI-native vendors can make search, context assembly, and investigation more adaptive. Buyers should judge the combined operating outcome, using vendor origin as context rather than as a proxy for capability.

Category boundaries will continue to blur as vendors use terms such as digital risk, digital trust, external threat intelligence, brand protection, fraud intelligence, executive protection, and AI-native DRP for overlapping functions. Clear operational requirements will matter more than the label attached to the product.

The longer-term direction is Digital Trust Operations, an environment that maintains reliable context about the organization’s public entities, understands threats against the trust relationships around them, and coordinates responses across security and business teams. DRP is an important route into that future because it already owns many of the external signals and remediation relationships, but the market will earn the expansion only by showing measurable reductions in analyst effort, time to campaign understanding, recurrence, and business harm.

Conclusion

The DRP category reset is an operating-model change. Organizations still need to find fake domains, phishing pages, impersonating accounts, leaked credentials, counterfeit listings, fraudulent apps, and other external abuse. They also need to understand how those artifacts connect, which business entity and trust relationship are under attack, who must respond, and whether the campaign remains active after action.

External trust operations provides that structure. It organizes the program around protected entities, campaign analysis, coordinated response, evidence continuity, and verified closure. The model also gives CISOs a more useful way to evaluate vendors because it connects technical capability to operational performance and business harm.

A DRP program is moving in this direction when it can discover unknown abuse, explain why it matters, map the wider campaign, route action with the required evidence, and prove that the active threat pathway has been closed. If those steps still depend on manual reconstruction across disconnected tools and teams, the organization has an external trust operations gap. The strongest vendors will help the organization close external trust threats faster, with better context, less coordination burden, and stronger evidence that the risk is actually resolved.

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If you lead a security operations function today, you already know the feeling this report starts from. The market is loud, crowded, and hard to tell apart. Triage that felt novel eighteen months ago now ships inside almost every SIEM, EDR, XDR, and SOAR product you already own. New logos arrive every week. The acronyms multiply faster than anyone standardizes them. And underneath the noise sits a harder problem that the noise is hiding: your team can now understand an incident faster than ever, and still cannot act on it any faster than before.

We call this the action gap, the distance between a validated incident and a safe, verified, state-changing response an organization is willing to stand behind. The first wave of AI in the SOC compressed investigation. It did very little for the part that actually changes risk, which is response. Knowing the threat exists is only half the battle.

Security practitioners and vendors alike have tried to address the action gap in their own ways, but there hasn’t been a model that can be applied to any environment or any level of maturity to let practitioners close that gap. That’s why we’re introducing Trusted Security Response Operations (TSRO): the operating model through which a security team grants software bounded authority to recommend, prepare, execute, and verify specific response actions, under explicit requirements for evidence, policy, credentials, scope, and accountability.

Building on SACR’s AI SOC Research

We developed this report as a continuation of our coverage of the AI SOC market. Revolutionizing Security Operations: The Path Toward AI-Augmented SOCs explored how AI and agentic systems were entering investigation and analyst workflows, while SACR AI SOC Market Landscape For 2025 mapped the expanding vendor landscape as the category took shape. This report builds on that foundation by defining the Trusted Security Response Operations model and the Trusted SOC to help CISOs and security leaders move from faster investigation to trusted, governed response.

AISOC Market Map

These reports reflect extensive primary and secondary research over the past several months: structured interviews with 20 security leaders and practitioners, briefings and live demonstrations with 23 vendors, and targeted surveys across the market. Peer trackers such as SecOps Unpacked now map close to 140 vendors touching this space. SACR assesses more than 60 of these as pure-play AI SOC vendors. Our market analysis analyzes a focused set of 18 platforms in depth.

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How to Use This Report

• This Part 1 focuses on building the Trusted SOC, defines the problems faced today, establishes the CISO operating model for addressing them, and explains our rationale behind it.

• Part 2 of our report focuses on vendor market categorizations and market rankings. The accompanying market analysis applies the model to reality, ranking a focused set of vendors across the three operating environments we see the most:

  • A mature regulated SIEM-centric enterprise SOC where the SIEM remains the system of record.

  • A Hybrid mid-market SOC with a leaner team operating across a mixed SIEM and data-lake stack.

  • An engineering led cloud native data lake SOC that prioritizes direct data access and lightweight administration.

Trusted Security Response Operations (TSRO)

We talked about the Trusted Security Response Operations (TSRO) earlier in our report. Using the TSRO model, practitioners will be able to achieve the outcome of a Trusted Security Operations Center (SOC). A Trusted SOC is one that has earned the authority to take specific actions through demonstrated decision quality, controlled execution, and verified outcomes.

The single most important reframe in this report is this. You are not making one decision about whether to trust AI in your SOC. You are making a portfolio of narrow delegation decisions, one action at a time, each of which can be granted, expanded, narrowed, or withdrawn on evidence. That is the difference between a marketing question, “Can we trust AI?” and an operating question you can actually govern: “Which action, under whose authority, based on what evidence, and verified how?”

• What changed. AI improved triage, investigation, summarization, and enrichment to the point where those capabilities no longer differentiate a platform. Trust in automated response is now the binding constraint on adoption, and therefore on the return you get from any AI SOC purchase.

• Category definition. A Trusted SOC is not a product you buy or a badge a vendor wears. It is a dynamic description of a SOC that has earned action-specific authority through evidence, controlled execution, and verified performance.

• Buyer takeaway. Treat the Trusted SOC as the outcome of governed delegation, not a feature list. Require every vendor to show, per action, what the system can recommend, prepare, and execute today, how that authority is constrained, and how the outcome is verified with proof after execution. Discount any answer given at the level of the whole platform.

• Market prediction. SACR expects the Trusted SOC to become the North Star operating outcome through which the modern AI SOC earns the authority to act. As the market consolidates around platforms, the winning question shifts from how much a system can automate to how much authority it can be trusted to hold and prove.

What The Market In 2026 Tells Us

It’s important for us to start with the reality that security leaders are facing today, because our TSRO model and the Trusted SOC outcome only earn their keep when they address the problems people face today. Five recurring pressure points came up in nearly every conversation we had:

1. Fragmentation. The systems that investigate an incident are still separate from the systems and processes that authorize and execute a response. Evidence lives in one place, authority in another, proof in a third.

2. Noise. Every vendor demo looks similar. Summaries, enrichment, entity extraction, and guided investigation now present a nearly identical analyst experience, even when the evidence handling and the response authority underneath differ enormously.

3. Lack of differentiation. Because investigation has commoditized, front-of-funnel capability no longer separates products. The real differences have moved downstream, into governance, execution, verification, and proof, which are exactly the things a demo does not show well.

4. Too many players. Independent trackers now count well over a hundred vendors claiming a capability here, and the number keeps climbing. Buyers cannot evaluate a hundred products. They need a way to reduce the field to the handful that fit their environment and the specific actions they intend to delegate.

5. No standard definitions. Vendors market AI SOC, agentic SOC, autonomous SOC, ISOC, XDR with agents, AI-native MDR. Analyst firms have not converged either. Gartner has introduced an Integrated Security Operations Center (ISOC) category, while Forrester kept the XDR name and added agentic systems as a distinct evaluation criterion, with SIEM replacement treated as real rather than experimental. When the taxonomers disagree, the buyer pays the tax.

These pressure points highlight two major structural shifts taking place:

1. Category consolidation. Every AI SOC conversation has become a consolidation conversation. Buyers are rarely satisfied with triage alone; they want to reduce or replace SIEM, SOAR, and MDR costs at the same time. Vendors have responded by expanding out of the triage middle: some move left into detection engineering, threat hunting, and even SIEM, while others move right into response, automation building, and managed services. Data-pipeline vendors are moving up the stack into detection and AI SOC as well. The line between a pure-play AI SOC and an AI SOC capability bolted onto an incumbent platform is blurring, and for many buyers it is no longer the deciding question. What matters is whether the capability is a genuine, governed response engine or a checkbox.

2. Urgency. For two decades the SOC was organized around a human sitting at the center of every decision that matters. That model assumed human judgment could keep pace with the threat. Frontier models in the hands of adversaries have started to break that assumption, compressing reconnaissance, exploitation, and lateral movement toward machine speed. The industry, including several vendors building operating-model frameworks of their own, now argues that bolting AI onto a human-speed process only relocates the bottleneck. We agree with the diagnosis, but our emphasis is different: speed without governed authority is a liability. The answer to a machine-speed adversary is a response that is fast, bounded, evidence-based, and provable. That is precisely what TSRO is designed to produce.

The Practitioner Signal

Our interviews with security leaders surfaced the following themes often enough to shape our analysis:

• The context moat. Security telemetry alone rarely contains enough to justify a response. The same behavior means different things in a media company and a regulated bank. Practitioners consistently told us that the platforms worth trusting are the ones that let an operator inspect the context behind a conclusion, add what is missing, challenge the hypothesis, and watch the recommendation change.

• Detection quality is critical. Several AI SOC vendors are adding detection engineering and even SIEM capabilities, partly because weak detections upstream cap the value of any downstream investigation or response.

• Proof lags ambition. Vendors define investigations, actions, and time saved differently, so headline metrics do not compare. The most credible teams measure at the level of the individual action, through execution and verified outcome, not at the level of the whole platform.

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The Wider SOC Architecture

SACR’s 2025 research described the SOC as a layered architecture spanning the data fabric, storage and detection, and response and automation. Those layers remain relevant, but the boundaries between them have become less stable. The most important architectural change over the past year is that capabilities previously purchased and operated separately are being pulled into a more continuous operating layer. AI SOC vendors are moving upstream into detection engineering, threat hunting, security data, and SIEM. SOAR vendors are bringing model-based investigation and decision support into their workflows. Data-pipeline vendors are adding detection and AI-assisted operations, while incumbent platforms connect their own telemetry and analytics to enforcement. Buyers are now evaluating how far a provider can carry an incident through the workflow and which existing costs or systems can be consolidated.

Reinforcing Detections

This expansion has pulled detection engineering back into the AI SOC discussion. The first generation of products relied on alerts from external sources, while newer platforms generate and tune detections in addition to the traditional AI SOC capabilities. This shift is a natural market response to a recurring theme in our research: weak upstream signals and poor context correlation undermine the investigation and response decisions built on them.

Investigation and Response Consolidation

Investigation and response have also begun to shift to a continuous runtime across the traditional layers of the SOC. Evidence collection, case development, decision-making, and execution were historically distributed across different tools and human handoffs. Modern AI SOC tools are increasingly attempting to maintain a case state across that path, mixing AI reasoning and deterministic automation to take dynamic and repeatable actions.

This report focuses heavily on this theme. Vendors approach this layer from three architectural starting points:

1. AI-native platforms begin with dynamic evidence collection, case development, and decisioning.

2. SOAR-derived platforms build on established strengths in workflow automation, case management, and repeatable deterministic execution.

3. Platform-consolidated vendors connect these functions to telemetry and control surfaces they already own. Each path creates distinct advantages and adoption burdens, but the required outcome is the same: a defensible path from incident to verified remediation.

AI’s role in security operations should remain distinct from deterministic automation. Repeatable enrichment, normalization, and containment are inherently safer and less expensive when handled through fixed logic, while AI provides greater value when the system must interpret ambiguous circumstances and recommend an action under uncertainty. Through TSRO, a mature architecture should make the division between AI and fixed logic visible, allowing operators to understand when a model is reasoning, when a workflow is executing, and how they work together to build the Trusted SOC.

Introduction of Governance

Governance now sits inside the architecture. Approval routing, policy constraints, credential separation, action scope, audit records, and outcome verification determine whether a system can move beyond investigation. These controls connect an AI-generated conclusion to a response action the organization is willing to authorize. TSRO applies at this boundary, giving security teams a model for granting and expanding authority one action at a time, based on the evidence and controls attached to that action.

Most buyers will end up with a hybrid SOC architecture. An independent AI SOC layer can sit over the existing stack, while some organizations will consolidate around a platform that already owns telemetry and enforcement. Others will keep a SOAR-derived execution layer or have a managed provider operate the capability. Few will replace every component at once. The practical questions are where the incident’s evidence and case state live, which system holds the authority to act, and how the organization proves the intended change occurred.

How SACR Defines the Market

The expansion of AI SOC across detection, investigation, response, and service delivery has made the category harder to define under a single term or product shape. Vendors use terms such as AI SOC, Agentic SOC, Autonomous SOC, Integrated SOC (ISOC), XDR with agents, and AI-native MDR to describe capabilities that increasingly overlap. These labels are useful for identifying where a platform began and how it is delivered, but they provide limited insight into the authority an organization should grant it.

SACR uses AI SOC to describe the broader technological environment and market. Within that environment, Trusted Security Response Operations defines how a security team governs software authority, and the Trusted SOC is the resulting operating state: a SOC that has earned authority to perform specific actions through demonstrated decision quality, controlled execution, and verified outcomes.

Defining Trusted Security Response Operations

Trusted Security Response Operations (TSRO) is the operating model through which security teams grant software bounded authority to recommend, prepare, execute, and verify response actions under explicit requirements for evidence, policy, credentials, scope, and accountability.

TSRO does not require or build toward blanket autonomy in the SOC. It can begin with recommended or prepared actions that remain subject to human approval. The key is the controlled process connecting evidence, decisions, authority, execution, and verification.

Security teams grant authority one decision at a time. An organization can often trust a system to close a clearly understood case while still requiring oversight or approval for more impactful state-changing action. These decisions form an authority portfolio that can expand, narrow, or be withdrawn as performance is monitored.

Trusted Security Operations Center

The objective of TSRO is to build a Trusted SOC. It is a dynamic description of a SOC that has earned authority to execute specific response actions through demonstrated decision quality, controlled execution, and verified outcomes. Trusted SOC should not be viewed as a category or market label applied to a series of products.

As the evidence supporting Trusted SOC actions becomes stronger, an organization can expand the system’s authority across different risk levels. The Trusted SOC is progressive by design, built through a growing portfolio of authorities bounded by policy and accountable to the people who own the security and business outcomes.

Where TSRO Sits Among Existing Frameworks

We did not arrive at this in an empty field, and it would be dishonest to present TSRO as if the market had said nothing about autonomy and trust. Several strong frameworks already exist. TSRO is designed to occupy the specific gap they leave open. It helps to see the market’s frameworks as answering three different questions.

1. Question one: where does a vendor play across the lifecycle? This is the mapping question, and the clearest work here is the SecOps Shift Map from SecOps Unpacked, which tracks how vendors that began in the triage middle have moved left into detection and SIEM or right into response and automation, with a separate lane for those adding MDR services. Analyst firms answer the same question at the category level, Gartner with the Integrated SOC (ISOC) label and Forrester by extending XDR to include agentic systems. These are coverage maps. They tell you what a platform touches.

2. Question two: what infrastructure makes autonomy possible? This is the architecture question. The clearest recent example is ExtraHop’s Context, Harness, Model operating model, which separates the real-time evidence an agent reasons on (context), the governed control plane that mediates and scopes every action an agent may take (harness), and the swappable model layer that does the work. Its companion idea, an open alliance of interoperating best-of-breed layers, argues that no single vendor delivers the whole model. This is an infrastructure blueprint. It tells you how the pieces fit.

3. Question three: how independent is the agent, in the abstract? This is the autonomy-ladder question, and it has both academic and industry versions: peer-reviewed SOC frameworks that define five levels of AI autonomy mapped to human-in-the-loop roles and task-specific trust thresholds, the Cloud Security Alliance’s six-level agentic autonomy and control framework with boundary enforcement, and vendor progressions from human-in-the-loop to human-on-the-loop. Kaspersky’s Trusted Autonomy framing sits here too. These are capability tiers. They tell you how much a system could do.

TSRO answers a fourth question that the others assume: how does a specific organization earn, govern, and prove authority for a specific action, over time?

Two distinctions make that a genuinely different question rather than a rebrand.

• Authority, not autonomy. Autonomy is a capability a vendor claims. Authority is a permission a buyer grants. The autonomy ladders describe what a system is technically able to do. TSRO describes what a named organization has decided to let it do, on which action, on what evidence, with which owner accountable. The locus of control moves from the vendor’s roadmap to the buyer’s governance.

• A portfolio, not a level. Every framework above tends to place a platform at a level or in a layer. TSRO holds that a single platform occupies many positions at once: proven authority to quarantine confirmed malicious email, policy-bounded authority to block a confirmed indicator, approval-bounded authority to isolate an endpoint, and advisory-only authority for privileged identity actions, all in the same deployment on the same day.

The honest overlap is with the governance layer of the architecture frameworks. What ExtraHop calls the harness, a governed control plane that scopes and audits agent actions, is the technical substrate that a TSRO program needs. We see them as complementary. The harness is the enforcement mechanism. TSRO is the operating discipline that decides what the enforcement should permit, how authority is earned, and when it expands or is revoked. Infrastructure alone does not tell a CISO which business risks to accept. TSRO is the layer that does.

Where TSRO Begins

Many products improve an analyst’s understanding of an incident without affecting the response path. These capabilities accelerate AI SOC workflows, but they do not establish TSRO on their own.

TSRO begins when software enters the governed path to a remediation action. A product supports TSRO when it can define or prepare an action, route it to the proper authority, and contribute to execution and verified outcome at its granted stage. The clearest distinction is whether a product helps move the incident beyond completed investigation toward controlled, verifiable remediation.

The Buyer Problem: Why the Action Gap Persists

Even with visible product consolidation, the systems used to investigate an incident remain separate from the processes used to authorize a response in most environments. Even after the SOC reaches a conclusion, evidence from fragmented sources has to be translated into a proposed action and carried into a different operational process.

Ownership is fragmented in the same way. The SOC can identify a compromised account, but authority over directory, endpoint, and workload actions is distributed across other teams. The delay often has nothing to do with uncertainty about the incident. It comes from determining who can authorize the action, what evidence they require, and how to execute without creating new risk.

And that risk varies sharply by action. Quarantining an email is usually bounded and reversible. Isolating a production server or changing a firewall rule can interrupt critical operations. The decision has to weigh asset criticality, blast radius, and the available recovery path, and existing workflows rarely bring that context into the moment of decision.

Finally, the process leaves an incomplete record. Many systems can show that an action was initiated, but not why it was chosen, what evidence supported it, who authorized it, or how the outcome was verified. Without that record, an organization cannot compare performance across incidents or decide whether software has earned broader authority. TSRO closes these gaps by bringing evidence, authority, execution, and verification into one operating process. The buyer problem then becomes tractable: how to make delegation operationally safe, organizationally accountable, and defensible over time.

Different Actions Require Different Authority

• Email response is a common starting point because it is contained. A platform can inspect the message, correlate recipient activity, review related identity events, and prepare a quarantine. The affected systems are known, the action is usually reversible, and success can be verified across mailboxes.

• Identity response requires more organizational context. An unusual login may signal compromise, or it may be a known administrative jump host. The right response depends on privilege, active sessions, device state, business role, and access to sensitive applications. Incomplete context turns a reasonable response into an avoidable business disruption.

• Cloud containment raises the threshold again, because a credential, policy, or workload may support several dependent services. The platform needs service ownership, production criticality, credential scope, dependency information, and a recovery path before action can be delegated safely.

The point is that each action carries its own evidence, policy, and authority decision. The same platform may hold policy-bounded authority for email quarantine while remaining advisory-only for identity revocation and cloud containment.

Building the Trusted SOC: The TSRO Trust and Delegation Model

TSRO treats trust as an operational state that is earned and maintained. An organization does not decide that an AI SOC or automation platform is trusted as a whole. Trust is determined incrementally as the system recommends and takes specific actions. The accumulation of those trusted actions is what builds the Trusted SOC.

Understanding this distinction is important because the consequences of error change with the risk level of the action. Recommending quarantine of a clearly malicious email carries less operational risk than disabling privileged identities or isolating production workloads.

There are conditions that can limit the progression of trust. Weak data and telemetry can undermine investigations, allowing routine incidents to be handled reliably while ambiguous cases expose missing context, poor data quality, and uncertainty. Business accountability also remains with the CISO and security operators regardless of whether software recommends or executes an action.

The TSRO model addresses these conditions through a chain of evidence, decision, authority and action, proof, and improvement. Weakness in any part of the chain provides clear justification to limit the authority granted to the system.

The Five-Layer Trust Model

Evidence

The foundation of trust begins with evidence. The evaluated platform must collect the information needed to support a decision and preserve where it came from, how it was used, and when it was collected. Outdated or contradictory evidence should be clearly annotated, along with the absence of evidence. A lack of evidence should not be treated as justification for safe action; it should be a warning sign.

Decision

The evidence collected supports the decision layer. A defensible decision surfaces the leading explanation of the incident, how the evidence supports it, uncertainty, and plausible alternatives. The decision layer should give the operator enough information to challenge the conclusion and understand what additional context could change the outcome.

Authority and Action

The action layer governs both authority and execution. It defines who can authorize a response and the target, scope, execution method, stop conditions, and recovery options associated with it. An evidence-supported decision can still produce a harmful outcome if authority or execution is not properly controlled.

Proof

The proof layer connects the decision and action to the resulting outcome. It records what was authorized, what was executed, whether the action achieved the intended objective, and whether rollback or recovery is required. A narrative record supports accountable human review, while structured output supports measurement and comparison across incidents.

Improvement

The improvement layer uses structured outputs to inform future operations. Details about accepted and rejected recommendations, failures, human overrides, and successful actions can drive updates to policy and workflows. This layer closes the loop across the five layers of trust and supports continued improvement.

Stages of Graduated Authority

We designed the stages of authority around the conditions that exist today in the market. Our surveys and interviews showed a consistent progression from recommend-only workflows through approval-required and policy-bounded actions. The data showed broader support for partial autonomy, while a fully autonomous or “lights-out” SOC remains constrained by trust, ownership, and business process.

Stage 1: Advisory Authority

The platform gathers evidence, develops a conclusion, and proposes an action while humans retain decision and execution authority. This stage tests investigation quality, relevance, and the platform’s ability to explain uncertainty without placing production systems at risk.

Stage 2: Approval-Bounded Authority

The platform prepares the action, defines the target and scope, presents the supporting evidence, and routes approval to the accountable owner. Human authorization remains part of the execution path, while much of the delay and manual handoff can be removed.

Stage 3: Policy-Bounded Authority

The platform may act for approved action types, assets, identities, scopes, and risk tiers without case-by-case approval. Policy replaces the one-off decision while preserving accountability. Clear stop conditions, exception paths, rollback, and verification are required.

Stage 4: Proven Authority

The organization maintains delegated execution authority for a specific action because evidence quality, decision accuracy, control reliability, false-action rates, rollback performance, and outcome verification have remained within accepted thresholds over time. Proven authority does not mean unrestricted control. High-impact actions may remain approval-bounded permanently.

The Authority Portfolio

An organization will usually operate at several stages of authority simultaneously. It may grant the same platform proven authority to quarantine confirmed malicious email, policy-bounded authority to block confirmed malicious indicators at selected control points, approval-bounded authority to isolate endpoints, and advisory authority for privileged identity actions.

Together, these action-specific decisions form the authority portfolio. Each individual authority should identify:

• Eligible incident and action

• Required evidence

• Decision threshold

• Action scope

• Accountable owner

• Blast radius assessment

• Rollback and recovery method

• System’s performance history on similar actions

The maturity of the Trusted SOC is determined by the breadth and reliability of this portfolio. Authority may expand as records support it, narrow when conditions change, or be withdrawn after a failure.

How Authority Varies by Action Type

Low-risk response actions can often move into policy-bounded automation first. Selected email quarantine, session termination, and temporary blocks on confirmed malicious indicators are usually reversible and have limited business impact. The main control requirements are accuracy, verification, and a clear audit trail.

Medium-risk actions should usually begin with approval-bounded execution and expand only after validation. Endpoint isolation and narrow containment can reduce risk quickly but may disrupt users or systems. The evidence threshold, owner, and rollback path should be explicit.

High-risk actions should remain advisory or approval-bounded until the platform proves decision quality, scope control, recovery, and audit-grade proof. Privileged identity revocation, broad cloud containment, and network policy changes can create business-wide effects. Evidence requirements should match the consequence of error, and automation should expand only when the proof supports it.

Failure Modes and Design Risks

The authority portfolio must respond to failure in practice. A system that performed reliably under one set of circumstances can encounter new data sources, attack techniques, and organizational constraints. TSRO therefore requires organizations to identify where the trust chain breaks and adjust authority appropriately as the environment changes.

Evidence-layer failures include missing, incorrect, stale, or poorly normalized data. Business context used by AI investigations can also be improperly correlated or maliciously modified by attackers. Source controls and rules for handling incomplete context are required before evidence can be trusted to support an action.

Decision-layer failures include unsupported claims, inflated confidence levels, and failure to consider plausible alternatives. They can be hidden when an investigation is compressed into a summary. Evaluators should test ambiguous incidents, conflicting evidence, and cases outside a vendor’s common demonstrations to assess reasoning in non-standard situations.

Execution-layer failures include an inability to complete the state-changing remediation, excessive permissions, incorrect targets, and API errors. Confirmation that a task ran through an API does not prove that the security objective was met. Verification of the completed objective is critical to evaluating the execution layer.

These failures should directly influence the authority portfolio. Consistent collection of metrics and evidence provides security operators with the basis for granting, adjusting, and revoking authority.

Measuring the Trusted SOC

The authority portfolio depends on continuous measurement. Organizations need evidence that a system continues to meet the thresholds attached to each delegated action, along with clear signals for when authority should expand, narrow, or be withdrawn. The Trusted SOC measurement stack connects technical performance, reliability, response outcomes, and operating economics to those decisions.

The most useful unit of measurement is the individual response action. Platform-wide automation rates combine activities with very different levels of risk and operational value. Quarantining an email, isolating an endpoint, and revoking a privileged identity should not be counted as equivalent forms of automation.

Measuring the Response Path

Time from detection to verified remediation remains an important measure because it identifies when the state of the environment changed. It should be divided into detection, triage, investigation, approval, execution, verification, and documentation.

This phase view is important because a single mean-time-to-respond figure can conceal the actual operational bottleneck. A platform may reduce investigation time while approval or execution time remains unchanged. Breaking the process into phases shows where technology created value and where organizational ownership or workflow continues to introduce delay.

The same approach should be applied to verification. An action is not complete when an API call returns a successful status. Endpoint isolation must be confirmed, and a malicious email must be removed from the intended mailboxes. Mean time to verify remediation measures the period between execution and confirmation that the security objective was achieved.

Measuring Authority and Human Review

Delegated execution should be reported by action type and authority stage. The organization should know how many actions remained advisory, how many required approval, how many executed within policy, and how many operated under proven authority.

Recommendation acceptance rate shows how often analysts or system owners agree with the platform’s proposed response. Rejection and override reasons provide more useful information than the acceptance rate alone. They should distinguish incorrect conclusions, missing context, business exceptions, and reviewer preference.

This distinction helps the organization identify the source of disagreement. A rejected recommendation may reveal a product failure, but it may also expose incomplete data or a policy that does not reflect current operations.

Measuring Safety and Recovery

False-action rate should include actions that were incorrect or unsupported by the available evidence. It should be calculated separately for each action type because the consequences of error vary significantly.

Rollback and exception rates show how often an action must be reversed, escalated, or completed outside the normal process. These measures should capture both technical and operational failures, including incorrect targets, approval errors, and unintended business impact.

Recovery performance is also important. An organization should know whether the platform identified the failure, initiated the correct recovery path, and restored the affected system. A low failure rate provides limited assurance when the system cannot recover safely from the failures that do occur.

Metrics to Approach with Caution

Broad mean-time-to-respond measures often combine too many phases to identify where improvement occurred. The percentage of alerts handled automatically may include duplicate suppression, enrichment, ticket creation, or low-risk closure without distinguishing state-changing response.

Analyst-hours-saved claims require a baseline workflow, defined task boundary, sample period, and quality control. Time saved has limited value when the work must be repeated, reviewed extensively, or corrected after an unsafe action.

A platform-wide autonomy percentage is particularly weak. Authority is granted to specific actions under defined conditions, not to the platform as a whole. Activity metrics become meaningful once they can be connected to a measurable operational outcome.

The Economics of AI-Driven Operations

Once response is measured at the action level, the organization can evaluate its true cost. The cost of AI-driven security operations extends beyond the platform license. It includes:

• Model inference

• Agent runtime

• Data movement

• Storage

• Retrieval

• Connector development

• Workflow design

• Implementation

• Credential governance

• Testing

• Human review

• Exception handling

• Ongoing maintenance

Failures add cost as well. An incorrect action may consume analyst time, disrupt users, or create business impact. A platform with a low license price can still produce an expensive operating model when integration and oversight requirements are high.

AI also changes the cost structure of security work. Security telemetry and operational artifacts are converted into model input. Costs increase when several agents process the same evidence, context is rebuilt at each step, tool responses are unnecessarily verbose, or the system retries and validates work without maintaining a reusable case state.

More capable workflows will require larger contexts, longer reasoning paths, additional tool calls, multimodal evidence, and repeated validation. Each may improve decision quality but will also increase consumption. Complex incidents can consequently cost substantially more than routine cases, making a simple average cost per investigation an unreliable forecast of production spend.

Commercial pricing can further obscure the relationship between cost and value. Flat platform fees may include usage ceilings, overage rates, or model-routing policies. Per-agent, per-investigation, per-action, and token-based pricing all tie costs to different forms of activity. Buyers need visibility into model selection, context size, and quality thresholds to understand how costs will change at production scale.

Economic discipline depends on using the appropriate method for each task. Fixed enrichment, normalization, ticket creation, approval routing, and repeatable response steps often fit deterministic automation. Model use should be concentrated where interpretation, adaptation, or judgment materially improves the result. Efficient retrieval, reusable case state, selective context, and observable model routing reduce duplicated work without weakening decision quality.

Organizations should measure model and agent spend by use case, repeated retrieval and retry rates, the proportion of work handled deterministically, and the cost of human review, failed actions, and verification. Cost per investigation remains useful for workload analysis, but cost per verified remediation is the stronger operating measure because it connects the full cost of the response process to a confirmed reduction in risk.

CISO Implications and TSRO Adoption

The CISO Decision

The CISO’s practical decision is which response actions can be delegated today, what evidence is required to maintain that authority, and what performance would justify changing it.

Those decisions will vary by organization. A financial institution, healthcare provider, technology company, and public-sector agency may reach different conclusions about the same action because their operating environments, regulatory obligations, and tolerance for disruption differ. TSRO provides a common decision structure without prescribing one acceptable level of authority.

The CISO must also determine who can grant or change that authority. Security may own the incident, but it does not always own the affected identity, application, or business process. Authority must reflect the organization’s actual accountability boundaries.

Adoption as Controlled Expansion

Adoption should begin with a small number of response actions whose scope, owner, evidence requirements, and expected outcomes can be clearly defined. The first use case should be meaningful enough to test the operating model but bounded enough that errors can be identified and recovered safely.

The initial evaluation should include normal incidents, benign anomalies, incomplete evidence, edge cases, and situations that cross team boundaries. The evaluation should surface disagreements between analysts and the platform, revealing missing context, inconsistent practices, and policy that needs clarification.

A time-bounded pilot using a representative set of cases can provide an initial baseline, but elapsed time and case volume are not sufficient on their own. The evaluation must include enough diversity to test uncertainty, escalation, ownership, and proof. A platform that performs well on repetitive phishing cases has not necessarily demonstrated readiness for privileged identity or cloud actions.

The authority stages established earlier in the report should operate as decision gates. Advisory operation tests the quality of the evidence and recommendation. Approval-bounded execution tests action preparation, scope, routing, and execution. Policy-bounded authority tests whether those controls remain reliable without case-by-case authorization. Proven authority depends on sustained performance rather than completion of a predetermined pilot period.

Expansion should follow the evidence, not a deployment schedule. An action may advance, remain at its current stage, return to an earlier stage, or be withdrawn. Different actions supported by the same platform will progress at different rates.

Governance Ownership

TSRO does not transfer accountability from the organization to the product. The SOC may own the investigation and recommend containment, while the teams responsible for identity, cloud, endpoint, applications, or affected business processes should participate in defining the conditions under which those actions may be delegated.

A cross-functional authority group should oversee material changes to the authority portfolio, review failures and recurring exceptions, and resolve ownership conflicts. Its role is to govern policy and authority, not approve every incident.

Build, Buy, or Hybrid

The three architectural paths described earlier—integration through an AI-native platform, extension of existing automation, and consolidation within a broader security platform—create different build, buy, and hybrid decisions. Each path can support TSRO when the organization retains control of its authority portfolio.

Building internally may fit organizations with mature SOC engineering, strong data and detection foundations, durable AI development resources, and workflows that commercial platforms cannot support. It can provide greater control over models, context, routing, deployment, and process design. The long-term burden includes maintenance, evaluations, model changes, and exception handling after the original development team moves on.

Buying may fit organizations that need packaged integrations, case workflows, and a faster path to production. The buyer still needs to determine how much configuration and service support the platform requires, whether it preserves existing investments, and whether its governance model fits internal ownership boundaries. A platform that deploys quickly but requires a new data architecture or operating process may create costs elsewhere.

A hybrid model will likely be the most common. A vendor platform may provide connectors, workflow, policy enforcement, and proof while the organization retains internal models, custom automations, decision logic, and managed context. Hybrid design is most effective when responsibilities are explicit and the organization does not maintain two overlapping control planes.

Regardless of the technical path, the organization must retain ownership of its authority portfolio. Vendors can provide the infrastructure for evidence, decisioning, execution, and proof. They cannot determine which business risks the organization should accept or who remains accountable for the outcome.

Conclusion

The shift from AI-assisted investigation to governed response changes how AI SOC maturity should be judged. More alerts summarized, cases investigated, or workflows initiated may improve productivity, but those measures do not establish whether software can be trusted to act. Maturity is demonstrated when the organization can define an authority, control how it is exercised, and verify that the action produced the intended outcome.

TSRO provides the operating model for managing that progression. Each authority is tied to a specific action, operating context, evidence requirement, policy owner, execution scope, and performance record. Authority expands when the evidence supports it and can be narrowed or withdrawn when conditions change. The resulting portfolio gives the organization a more defensible measure of progress than a platform-wide autonomy percentage.

The resulting Trusted SOC defines exactly what software is permitted to do, under whose authority, within which boundaries, and with what evidence of success. Humans retain accountability while intervening more precisely. Trust is earned through the organization’s ability to turn security decisions into controlled, verified, and accountable outcomes.

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Endpoint security is entering its next architectural transition. For nearly fifteen years, Endpoint Detection and Response (EDR) has been the foundation of endpoint defence for enterprises. It was designed for an era where attackers executed malware, launched processes, and modified files that operating systems could observe. That assumption is rapidly breaking down. Companies like Crowdstrike EDR, SentinelOne EDR, Palo Alto Networks EDR earned that position by answering the question of its era: what did this program do on the machine? EDR watches the operating system. It logs when a process starts, and when a file is written, then matches those events to known attacker behavior. It worked because anything dangerous had to run as a visible program the operating system could see.

This assumption is now failing with AI usage in enterprises today. This is because the way work happens on the endpoint is changing. Three shifts separate the 2026 endpoint from the one most security programs were built for prior to 2023:

• The rise of AI coding agents: In 2023, AI in the developer’s tools meant autocomplete that suggested code a human accepted line by line. In 2026, agents such as Cursor, Claude Code, and Copilot agent mode read the codebase, run shell commands, install packages, and complete tasks directly on the machine using the developer’s own permissions.

• Capabilities connect through a live protocol, not an installer. The rise of Model Context Protocol (MCP), which did not exist before late 2024, lets agents plug into tools, data, and internal systems at runtime. Each connection is a new link in the supply chain that never appears in a traditional software inventory.

• Everyone became a builder (not just engineers). AI tools for employees, Low-code and no-code frameworks can now let someone in finance or operations assemble a working application, workflow, or agent in an afternoon, often with no ticket, no review, and no security checkpoint.

The pattern underneath all three is the same. The risky action is no longer a file the system can scan. It is an instruction written in plain language, carried out by software acting on a person’s behalf, often across steps that each look harmless alone. The endpoint has stopped being a device the operating system governs and become the point where people, agents, applications, and data meet. Securing it means governing that interaction as it happens, not reviewing the process after it ends. These interactions often occur above the operating system, leaving conventional EDR with only a partial view of what is actually happening.

SACR believes this shift will create a new architectural category for the next decade: Endpoint Control and Prevention (ECP).

ECP covers a new vector that EDR’s today miss. They focus primarily on detecting malicious processes after execution; ECP expands endpoint security into a runtime control plane capable of governing AI agents, browser activity, application workflows, identity context, and sensitive data before attacks complete.

The market landscape ecosystem is emerging. We have seen an explosion in companies on the market moving to solve the problem. Specialized vendors are expanding endpoint security across software posture, application enforcement, AI runtime visibility, behavioural analysis, and data-centric controls. Over time, these capabilities are likely to converge into broader endpoint platforms.

• EDR to ECP players: CrowdStrike, Palo Alto Networks, SentinelOne have moved to cover the new agentic market complementing their EDR solutions.

• Emerging ECP native key players include: Neo Security, Bay Security, Bloom Security, Glow Security, Pluto Security, Origin HQ, Ent, Bold Security, NeutralTrust, and Cyberhaven, which define individual zones through purpose-built architecture. The vendors that already span several zones point to where the category is heading, toward one converged control plane rather than five separate tools. These are leading the charge to solve this market issue. We have partnered closely with the key players to perform our analysis, which is attached in the report.

On the left is the EDR market as it stands today, competing on process and file telemetry that has largely commoditized. On the right, that same market fans out into the five zones where endpoint defence is now expanding, from software posture and supply chain governance at one end to data-centric enforcement at the other. The connective idea is that EDR becomes ECP: an extension of the endpoint, not a replacement of it.

The Report Summary: This report makes five observations.

1. The endpoint is becoming the primary enforcement point for AI. Security controls must move closer to where prompts, tool calls, browser actions, and agent decisions actually occur. In 2026, more of this activity is happening at the endpoint.

2. EDR is approaching an architectural ceiling. Process and file telemetry remain essential but no longer provide sufficient visibility into AI-driven workflows, browser sessions, SaaS applications, and non-binary software.

3. Context becomes the new detection engine. Modern security requires understanding who initiated an action, what prompted it, which tools were used, and whether the resulting behavior aligns with legitimate intent.

4. Prevention increasingly replaces response. Rather than terminating processes after compromise, next-generation platforms intervene earlier using software governance, workflow controls, behavioral guidance, and inline enforcement.

Endpoint Control and Prevention should not be viewed as the replacement for EDR. Instead, it represents the next stage of endpoint security, extending protection from the operating system into the interactions between users, AI agents, applications, identities, and data that increasingly define enterprise risk.

Endpoint Control and Prevention (ECP) Thesis

Our thesis is that the core failure in EDR is structural. Defenders lack actionable context within the execution workflow. Traditional endpoint tools secured basic machine hygiene and website access, but the next generation must secure the active interaction flow between users, autonomous agents, and SaaS platforms. Endpoint defense must transform from a passive detection sensor into a strategic control plane for the expanded use cases. EPP and EDR protected the machine and EDR protected the processes and some scripts; today, the defense perimeter must expand. The next-generation Endpoint Control and Prevention (ECP) endpoint protects the interaction of the critical flow between humans, AI systems, services, and data.

Market Definition: Endpoint Control and Prevention (ECP)

Endpoint Control and Prevention (ECP) is a pivot in endpoint security that reframes endpoint defense as a runtime visibility, operational workflow, and user contextual gap problem. ECP expands endpoint visibility beyond operating systems, files, and processes into developer environments, SaaS applications, AI tools, and agent frameworks. It secures these environments by monitoring the interactions and data flows between users, AI agents, applications, and command-line activities. Next-generation ECP focuses on prevention, enhancing context sharing and user-driven responses across emerging AI, SaaS, and data environments. It focuses on the governance of non-binary software, deeper application-layer execution context (especially for AI and data movement) for better user and agent behavioral characterization or judgement, and machine-speed intent recognition from AI to ameliorate precision action. All of which offers endpoints stronger attribution capabilities, allowing endpoints to transform from more passive detection and response-oriented sensors into more active control planes for the user, AI or agent for prevention, investigation or response actions.

Strategic Imperative: Transforming Telemetry into Decision Support

1. Application-layer discovery and posture control for non-binary software (extensions, plugins, models, MCP tooling, configuration risk)

2. Greater application user behavior visibility depth, governance and control by extending policies to the browser and its contextual telemetry.

3. AI Prompt-to-action attribution for agentic systems (what triggered an action, what tools were used, and what happened next)

4. Local semantic inference (often via small local language models SLMs and graph storage) to interpret intent and drive proportional controls and responses autonomously from back-end operations.

The outcome requirement is simple: Intercept risky flows and sessions before completion, reduce blast radius, and produce evidence that stands up in investigations and governance.

In scope

• Threat detection evolution for AI and local agentics

• Surgical intervention for lineage, evidence, and governance of users, data, and AI and agents

• Local application context and extension to non-binary configurations and unmanaged dark matter identities (secrets, certificates and other artifacts)

• User, Identity and agent telemetry, traceability and data lineage

• Trusted, zero trust access and software postures above the OS (e.g. visibility and control over local DevOps integrated development environments, IDE plugins, AI agent harnesses/frameworks)

• Identity and SaaS-mediated execution context (e.g endpoint and SaaS shared telemetry to shift more policy and threat detection from backend to frontend)

Out of scope

• Standard EDR feature updates focused on legacy malware, traditional endpoint focused indicators of attack (IOA) or indicators of compromise (IOC) indicators

• Standalone SIEM or SOAR platforms

• Network-layer-only controls

• General XDR bundles that primarily aggregate endpoint detection + response

Why is Endpoint Security being Re-Written?

Endpoint defense is being rewritten because decision points and context that can be derived and used are moving upwards in the stack. Context has taken precedence in a world of dispersed applications and interactions. In the near future, federated context from MCP will contribute significantly to both backend analytics and endpoint security as the threat landscape expands. Classic malware remains a threat, but many incidents now begin with a compromised session, a risky SaaS authentication (OAuth) grant, a compromised API secret, credentials, or a workflow executed legitimately with a stolen identity.

As adversaries bypass technical controls through credential attacks, session hijacking, and autonomous agent manipulation, the traditional telemetry stack encounters three terminal blind spots:

1. Invisibility of non-binary software: Browser extensions, plugins, and local models execute without traditional installations, leaving file-centric EDR blindsided.

2. Deficit in intent and attribution: EDR tools fail to distinguish between malicious injections and legitimate commands, lacking autonomous oversight and intent assessment.

3. Contextual blindness in execution paths: Encrypted connections, AI apps, and SaaS environments obscure user and application context from traditional tools.

Nefarious actors can now progress via autonomous AI agents and their delegated toolchains, where the file system becomes more secondary (or irrelevant) to the execution context or location of execution. This means shared context must come from new sources to make better, more localized decisions with enhanced visibility and context on the endpoint, giving security responders faster, real-time prevention, depth for incident response efficiency and enhanced precision. We depict the new evolution in the timeline graphic below.

Era Emergence: From EDR to Endpoint Control and Prevention (ECP)

Legacy EDR is failing because it targets symptoms and conditions rather than the underlying structural issues or overall execution contexts. By focusing on file system telemetry and process trees, traditional security boundaries miss the strategic shift up-stack to non-binary software, sessions, autonomous workflows and SaaS. ECP resolves this structural blindness by transforming the endpoint into an active control plane that monitors, participates and governs the interaction layer between humans, agents, and data.

Conceptualizing the evolution of endpoint defense transformation requires transitioning to a framework focused on establishing new architectural layers or zones, leading directly to improvement of endpoint defensive and prevention posture. By integrating advanced visibility and control across these areas, organizations can re-align with contemporary threat landscapes and optimize the use of multi-model context, behavioral intent, and diverse data interactions to drive proactive defensive and prevention-focused decisions.

The Eras of Endpoint Security

Endpoint security has evolved through three distinct eras, each triggered by the structural failure of the previous architecture.

The Antivirus Era (late 1980s to late 2000s): Protecting the File

The first commercial endpoint security products industrialized the idea of maintaining a database of signatures for known malicious files. This worked while malware was a static file, the industry was in a race between virus authors and signature distribution. The model collapsed under the pressure of polymorphic malware and targeted attacks like Operation Aurora (2010), which demonstrated that adversaries could bypass signatures entirely by using bespoke, never-before-seen implants.

The EPP Era (late 2000s to mid 2010s): Protecting the Machine

The industry’s first response was to bundle security tools, antivirus, firewalls, and device control into Endpoint Protection Platforms (EPP). While EPP improved prevention, it lacked visibility into what occurred when prevention failed. Breach investigations of the era consistently revealed that intruders remained resident for months, leading to an assume-breach doctrine and the realization that the decisive capability of the EPP era was not blocking malicious files, but rather observing endpoint activity.

The EDR Revolution (2011 to the mid 2020s): Protecting the Process

Endpoint Detection and Response (EDR) succeeded by recording everything, from process creation, file writes, and registry changes, into a cloud-hosted graph to identify adversary behavior. EDR’s dominance rested on one foundational architectural assumption: everything that matters on an endpoint executes as a process the kernel can see. As threats migrate up-stack to non-binary software, sessions, and AI agents, this reliance on kernel-level process visibility is reaching its limit, necessitating the shift to Endpoint Control and Prevention (ECP).

The ECP Redefinition (2026 through 2030): All we need is Context and Intent

The ECP redefinition phase marks a strategic pivot away from reactive process monitoring toward holistic, interaction-centric, SaaS and context-aware governance with user and agent intent awareness. This timeline emphasizes the necessity of newly emerging agentic workloads, managing high-speed autonomous execution, where the endpoint must evolve into an active control plane capable of verifying intent and securing non-binary and user execution contexts in real time.

Focused on Emergent areas such as:

1. The Human User (Shadow Builders)

2. AI Agents

3. Agentic Layer

4. Application and Workflow Layer

5. Identity, Session and Artifacts

The rapid proliferation of enterprise AI agents and automated supply chain vulnerabilities has intensified this crisis. Modern adversaries bypass OS-level signatures entirely by injecting malicious tools directly into shadow builder or developer ecosystems and agentic frameworks. These autonomous threats often operate with high-level permissions, independently navigating networks and executing custom, on-the-fly commands or building and executing their own code at machine speed. Recently, attackers have adapted part of their game; since so much net-new AI is being adopted on endpoints, threat actors are now targeting the new AI and agentic software supply chains to execute their nefarious acts.

Recent supply chain breaches highlight the urgency of securing this non-binary execution context:

• PyTorch Lightning PyPI Compromise (April 2026): Hijacked credentials allowed malicious library versions to harvest and exfiltrate environment secrets via automated CI/CD pipelines.

• Mercor-LiteLLM Upstream Breach (April 2026): A compromised routing dependency propagated downstream, exposing sensitive training pipelines and forcing immediate contractor suspensions.

• NX NPM Breach & Agent Hijacking (August 2025): Poisoned packages executed locally to hijack active AI tools, systematically hunting and exfiltrating SSH keys and API tokens.

• Hugging Face Weaponized Models (Ongoing): Malicious models embed reverse shells within unsafe serialization formats, instantly compromising the host endpoint upon model loading.

Assumption: AI agents and their arrival with Endpoint harnesses creates a cascade of new demands such as monitoring, assessment of risk, management and control, driving net-new technology adoption in endpoint security. These and the emergence of custom software generated on-demand by AI agents (even apps, scripts and services deployed locally on endpoints) become a new IT standard; legacy endpoint security metrics and even DevOps or DevSecOps metrics will also become obsolete on a standalone basis. One way to think of this is in the Builder Era (where all users become agentic shadow builders), federated development becomes the norm. This strongly shifts the value proposition of endpoint defense decisively from post-incident response to real-time execution control and proactive context governance.

To defend the modern enterprise, endpoint security must pivot from general user, software, and binary-driven monitoring and acquire higher-order contexts in the stack, helping unify disparate activities on the endpoint for enforcement pre prevention consideration. We need specialized oversight for autonomous agentic systems, integrated SaaS and the context created by users and agents, and visibility and control over the workflows connecting them. In older environments, investigators struggled to tie a user’s actions to automated agents they triggered. Today, as users increasingly become shadow builders spawning agents, writing and executing code via local chat interfaces and agentic harnesses, these new agents effectively become net-new bots on the internet. This is mandating evolution in adjacent areas (outside endpoint), for example, how we identify bots vs agents online. AI Agents often use similar tools to bot scraping services and other engagement tools that make them overlap with traditional malicious bots, representing the same dynamic and challenge to overcome on the endpoint. Even when operating under a user’s identity, actions taken by agents require a new forensic perspective and better context for decisions.

Effective defense now relies on our ability to provide user and agent context and enable rapid context sharing and enforcement mechanisms at machine speed. By adopting this approach, acceleration enables near-instantaneous security decisions, empowering both users and automated monitoring agents and other local sensory software to intervene and halt malicious behaviors the moment they arise. This shift opposes the traditional, slower-moving world of traditional breach-oriented detection and response focused on users or simple administrative functions or scripts (already representing a detection challenge). As enterprises weigh the benefits of control against the need for agility, the industry is moving decisively away from manual human-in-the-loop (HITL) processes and toward automated, proactive control and prevention.

In today’s environments, high-consequence actions rarely occur as simple file executions. They happen inside:

• Browser Sessions: Where copilots handle approvals and data movement.

• The Identity Plane: Where attackers leverage tokens, OAuth grants, and API keys.

• Delegated Toolchains and Self-Developed Agent software: Where agents run scripts, build their own tools on the fly and move data directly.

Historically, Endpoint Detection and Response (EDR) has failed to extend into these specific areas on the device. However, technical shifts, such as machine-speed risks and AI-powered attacks using the advanced capabilities of Mythos, are forcing a faster transition to higher speed, greater context, and prevention prioritized over the EDR detection and response heritage. Organizations that continue to rely solely on traditional detection and response strategies will fail, the landscape is being redefined into one of real-time, active control and prevention-focused defense.

Emerging ECP Value Proposition

The Endpoint Control and Prevention (ECP) value proposition centers on transforming endpoint defense from a passive, binary-centric sensor into an active control plane capable of governing the modern interaction lifecycle, whether user or agent. By shifting visibility and enforcement up-stack, beyond kernel-level activity and file-based telemetry, ECP restores defender advantage in an era of AI-driven, machine-speed attacks. It bridges the gap between legacy detection and the reality of autonomous workflows, providing the contextual evidence and granular, real-time intervention primitives needed to secure the critical flow between humans, identities, agents, and SaaS applications.

Critical Endpoint Control and Prevention Layers

• OS and Kernel (classic EDR center of gravity): This layer represents the traditional foundation of endpoint security, focusing on low-level system activity and kernel-mode monitoring to identify malicious behavior at the hardware-software interface.

• Process, file and memory (EDR telemetry & response primitives): These core primitives enable the detection of legacy file-based threats and post-incident reconstruction by analyzing how processes interact with the file system and system memory.

• Identity and session artifacts (tokens, OAuth, browser auth, session abuse): In ECP, visibility and context extend to the identity plane to protect against session hijacking and the theft of cryptographic artifacts in real time, at runtime: for example, tokens and API keys used in SaaS-mediated workflows.

• App & workflow layer (browser actions, SaaS actions, agent tool calls): Endpoint defense now necessitates runtime observability at the application layer, governing non-binary software such as browser extensions and IDE plugins where critical modern execution context resides, but is often not shared.

• Agentic prompt-to-action layer (prompt, tool, action traceability): This critical new layer provides full attribution for autonomous systems, linking natural language prompts to specific tool invocations and downstream actions to ensure governability at machine speed.

Endpoint and End-User Organization Trends Enabling the Shift

Modern end-user organizations are driving the shift toward Endpoint Control and Prevention (ECP) as traditional security perimeters dissolve. With the rise of hybrid work and the proliferation of “shadow builders”, which are often non-technical staff deploying custom AI-driven application development, workflows and SaaS applications. The endpoint attack surface has expanded beyond legacy and more stable binary and process-centric threats. To address these complex risks, organizations are prioritizing comprehensive visibility into application-layer interactions and autonomous agentic behaviors, necessitating a move toward granular, real-time control.

• Broad adoption of hybrid working and the rise of Shadow AI and Shadow Builders: The emergence of shadow builders and Shadow AI both represent a significant market driver for ECP, as non-technical staff increasingly use low-code and no-code AI agent frameworks tools and AI plugins to assemble automated tools and workflows. This decentralized development creates new third-party supply chain models directly on the endpoints or inside designated harness infrastructure for agents, necessitating visibility into non-binary software linked to browser sessions, extensions, and IDE plugins.

• Instrumentation is shifting down-stack while risk shifts up-stack. Kernel and eBPF-era telemetry improves what happens, but the differentiator is increasingly interpreting and governing what runs above the OS (extensions, plugins, packages, models, MCP tools and shadow supply chains).

• Shadow builder risks are now a first-class endpoint problem. Since non-developer users and teams can now assemble powerful workflows, build net new applications and deploy plugins and AI tools, security needs visibility into all of this new installable software, its permissions, composition risk, configuration and communications, which are no longer limited to just malware-style OS level indicators.

• Deep Posture Assessment and Identity Awareness are emerging. As per-endpoint detection commoditizes, budget and evaluation energy move to application posture, attribution depth, and real-time intervention primitives (warn/block/guide) that reduce blast radius.

Operational Reality: Aligning Defense with Machine Speed

We have entered the age of Mythos, which is an era defined by the industrialization of vulnerability exploitation at machine speeds. Automated models are compressing the time required to weaponize software flaws from weeks to ~seconds. With the emergence of models like Mythos and open-source frameworks like GLM 5.2, the cybersecurity landscape has transitioned into a high-speed race to automate both AI vulnerability discovery and remediation via threat management processes. This evolution renders traditional, manual patch cycles obsolete, forcing a strategic pivot toward continuous, autonomous prevention engineering and retooling of the flows of data and context across security systems more generally.

The failure of legacy defense stems from a structural misalignment with how work actually occurs:

• Execution has shifted from local binaries to SaaS-mediated workflows: Security telemetry cannot stop at the file system or process tree when the action and critical context occur in browser sessions, SaaS and API calls.

• Identity is still a primary chokepoint: Tokens, OAuth grants, and session artifacts like cookies or authentication bits are the new crown jewels, and their abuse is a primary vector for modern compromise.

• Non-binary attack surfaces are expanding: The real risk resides in extensions, IDE plugins, and agentic configurations that mutate daily; a compositional, use- driven attack surface can bypass legacy signature-based scanning.

• Autonomy has changed the unit of risk: We have moved from a user running a process to an agentic toolchain executing a workflow. These actions occur at machine speed, rendering human-in-the-loop triage a strategic bottleneck.

• MTTR requirements have collapsed: Cloud-backend analytics loops cannot keep pace with agentic misuse. Defensive logic must shift right to the endpoint runtime.

The Strategic Imperative: Securing the AI and Agentic Layer

Modern ECP architectures require deep visibility into the non-deterministic nature of local agentic infrastructures, including IDE copilots, local AI runtimes, and MCP-integrated toolchains. Governance must extend to the prompt-to-action trace to ensure full context, lineage, and verifiability for autonomous systems. Defenders must ensure that actions taken by local agents against remote APIs or SaaS platforms are properly contextualized, mediated, attributed, as well as mapping out what triggered an action, what tools were used, and what happened next. These are very important aspects of monitoring context to drive enhanced endpoint prevention and behavioral response decisions.

Small Language Models (SLMs) Arrive on the Endpoint

The transition toward AI PCs and Minis, along with an increase in shadow builders (unauthorized) or authorized users setting up local agent harnesses, is accelerating the need for deployment of local models, bringing agentic benefits to endpoint security. This development empowers local AI models and agents specialized in Endpoint Security to manage scaling agentic telemetry and localized or remote context, circumventing the cloud-pipeline expenses, latency, and contextual issues inherent in current setups.

Traditional, EDR-centric protection models are proving inadequate for modern defenders. In this report, SACR highlights a necessary paradigm shift: migrating full-stack context and security logic directly to the device. By leveraging local agents (Endpoint Defense Agents), Small Language Models (SLMs), and local graph databases, systems can achieve semantic and behavioral insight to improve threat prevention. This architecture facilitates instantaneous enforcement, circumventing the delayed, cloud-reliant detection pipelines characteristic of legacy EDR/XDR vendors that depend on central storage backends.

Endpoint Defense Agents Rise

Moving agentic inference onto the Endpoint hardware redefines the threat landscape, where local inference risks, data lineage and attribution emerge as the primary detection hurdles. Rather than simply evaluating if a process is malicious, defenders must identify the specific user, agent, workflow or tool and identify errant or malicious prompts that initiated an activity, confirming if it aligned with user intent. This establishes prompt, user and agent action lineage as the modern extension of threat detection from the traditional process tree.

Adaptive and Predictive Prevention Becomes the New Goal

Cloud-backend analytics and context enrichment loops can no longer keep pace with AI and agentic misuse, causing traditional MTTR requirements to collapse and forcing defensive logic to shift right to the endpoint runtime. Moving endpoint defense beyond traditional process and file centricity, this approach rejects standard signature or rules-based logic in favor of probabilistic, anomaly-based detection driven by local semantic inference. Endpoint security systems must evolve into proactive, predictive prevention engines and contextual sharing architectures. By leveraging expansions and preemptive modeling of advanced context, behavioral modeling and intent awareness of both user and agent, endpoints can enhance and anticipate agent-driven threats before they fully manifest, achieving millisecond-level mean time to response (MTTR). This proactive stance supports a self-healing zero-trust model, where the endpoint facilitates faithful action execution while continuously reducing the attack surface. By executing defensive logic directly at runtime via local SLMs, the endpoint can interpret user and agent intent to intercept risky or non-deterministic workflows at machine speed before any malicious intent can fully execute.

7 Critical Endpoint Control Moment Pillars Important for ECP

To effectively govern modern autonomous workflows and minimize the blast radius of agentic toolchains, security teams must embed defensive logic across the entire interaction lifecycle. Shifting to Endpoint Control and Prevention (ECP) requires moving beyond reactive, post-execution detection to establish high-fidelity visibility at each stage of the workflow.

The following pillars identify the critical control moments where ECP architectures must intervene to ensure policy adherence, verify intent, and mitigate risk before an action completes.

Endpoint Security Enforcement Control Moments

1. Before Installation (Pre-deploy gate): Focuses on proactive curation of the software supply chain to neutralize threats before they manifest on the device. By treating the endpoint similarly to an app store, this phase discovers and risk-ranks non-binary software components, such as browser extensions, IDE plugins, MCP servers, packages, and local models, ensuring configuration hygiene and preventing unauthorized dependencies from landing on the machine.

2. Before Action Completes: Involves gating risky behaviors, dangerous tool calls, or unauthorized data sharing mid-flow. Instead of relying on disruptive post-execution process termination, this control point uses inline, flow-level session evaluation to intercept actions at machine speed before they can fully complete.

3. During & After Execution: Builds runtime observability and behavioral baselines above the operating system layer. It continuously monitors active interactions, including natural language prompts, shell commands, tool execution, and network activity, using local semantic inference to detect intent-level anomalies and maintain complete forensic traceability.

4. Context Enrichment (Environmental Signal): Integrates deep identity plane and environmental telemetry, such as active session states, cryptographic tokens, OAuth grants, and API keys. This shared context connects endpoint activity directly to SaaS-mediated execution paths, providing the multi-layer visibility needed for continuous trust validation.

5. Data Understanding (Semantic Layer): Leverages on-device AI models and local small language models (SLMs) to interpret the natural language context of prompts and workflows. Shifting from rigid pattern-matching rules to probabilistic inference, this layer evaluates user and agent motivations to recognize risky prompt injection or unauthorized orchestration before execution.

6. At the Moment Data Moves: Monitors and intercepts sensitive data flows at critical interaction points, such as clipboard paste, file downloads, or model prompt submissions. By performing local AI classification directly at the endpoint edge, it successfully inspects cert-pinned or SASE-bypassed traffic that network proxies miss.

7. Final Policy Decisioning: Evaluates the combined signals of intent, posture, identity, and data behavior against organizational security rules to drive real-time, graduated interventions. Rather than defaulting to binary allow-or-block decisions, it enables proportional enforcement primitives such as surgical inline redaction, warnings, or context-aware user coaching.

SACR Security Zones for Endpoint Control and Prevention (ECP)

To evolve new use cases across the control moments pillars, we see emerging technology concepts and vendors evolving endpoint defense in several key emerging zones of technology and use case expansion, which requires transitioning from legacy, process-centric models to a robust architectural framework of ECP security zones. These zones serve as the blueprint for securing the modern, autonomous workflow by establishing high-fidelity visibility and control at the critical layers where risk now resides. By mapping defenses to these five distinct evolutionary focus areas, security teams can move beyond reactive detection, enabling proactive governance of user, agent, and data interactions at machine speed.

Zone 1: Software Posture & Governance

Concept: By treating the endpoint similarly to an app store, this layer controls the ingestion of various software components such as extensions, MCP servers, packages, models, and containers. Proactive curation of the software supply chain neutralizes incidents before they manifest by maintaining an active inventory and comprehensive posture assessment.

Operational Pillar

Shifts endpoint defense from reactive binary signature matching to proactive continuous governance of the non-binary software composition layer. This involves discovery and continuous risk-ranking of browser extensions, IDE plugins, and local model frameworks to establish an active inventory and continuous posture assessment above the operating system layer.

By treating the endpoint similarly to an app store, this layer controls the ingestion of various software components such as extensions, MCP servers, packages, models, and containers. The foundational premise is that because the vast majority of AI risk is acquired, proactive curation of the software supply chain can neutralize incidents before they ever manifest. Referenced as Endpoint Application Posture Management (EAPM) within SACR advisory frameworks and as a critical feature of ECP expansion, this paradigm and concept marks a strategic resurgence of classic OS oriented posture and control application control and configuration assessment with renewed focus on endpoint and agentic applications and supply chains.

While traditional allowlisting was historically constrained by operational overhead, intensive manual fine-tuning, and rapid policy challenges, AI completely redefines this resource-heavy dynamic. By deploying automated fleets of specialized agents, organizations can continuously analyze software behaviors and dynamically maintain allowlists at machine speed. Enforcement within this zone shifts the primary security discipline away from reactive detection engineering toward proactive policy engineering. It treats platforms as first-class citizens where configuration liabilities, such as a Claude instance, for example, with dangerous permissions enabled, are surfaced as high-severity posture anomalies rather than traditional malware events. The ultimate goal is to sustain an active inventory and comprehensive posture assessment of all non-binary dependencies functioning above the operating system layer, verifying configuration hygiene, access permissions, and the intricate workflows generated by shadow builders using AI agents before any malicious intent can execute.

Zone 2: Application Layer Enforcement

Concept: Sits at the interface between native apps and AI agents, governing layers like browser extensions, developer packages, MCP servers, and AI agent toolchains. It blocks dangerous tool-calls mid-flow or potentially risky data sharing without killing the session.

Operational Pillar

Enforce inline gating and session evaluation mid-flow to block dangerous tool calls or data sharing before completion, replacing disruptive process termination with surgical intervention embedded directly within the active workflow layer.

Sits at the interface between native apps and AI agents, governing layers like browser extensions, developer packages, MCP servers, and AI agent toolchains. It blocks the dangerous tool-call mid-flow or potentially risky data sharing without killing the session. The core bet is that at machine speed, where autonomous agents read natural language instructions, execute shell commands, edit files, and call APIs at a speed no human could perform or anticipate, traditional detection-and-response is operationally and structurally too late, and therefore must evolve to keep pace with emerging risks.

The rising prevention-first approach mandates that actions be gated using inline, flow-level, session evaluation, or data-layer enforcement before tool calls can fully complete. This strategy successfully replaces disruptive process termination with a more precise, surgical intervention embedded directly within the active workflow layer. Vendors in AI are hard at work continuously modeling both AI interactions to preemptively identify attacks while also expanding their ability to monitor deep workflows, and as DNS for Agents emerges in DNS land, most organizations still lack methods for executing more complex multi-layer agentic workflow recording and intercede. Traditional tools cannot block potentially harmful actions on both Application AI agents and their workflows together as a continuum, where the evaluation of intent and outcome behavioral analysis focuses on precision prevention.

Zone 3: Agent Runtime Visibility (AI-EDR)

Concept: This architectural layer builds its own above-OS telemetry and baselines of why actors behave as they do, rejecting standard signature or rules-based EDR logic in favor of probabilistic, anomaly-based detection driven by local semantic inference.

Operational Pillar

Provides full context and verifiability for autonomous systems by establishing absolute traceability from natural language prompts to downstream execution steps, tool calls, and system responses. It reframes the endpoint as an identity-centric control plane evaluating intent to mitigate credential theft and session hijacking through continuous identity-state validation and active blast-radius reduction.

This architectural layer builds its own above-OS telemetry and baselines of why actors behave as they do, shifting endpoint defense beyond traditional process and file centricity. It rejects standard signature or rules-based EDR logic in favor of probabilistic, anomaly-based detection driven by local semantic inference. The underlying bet is that novel AI-era attacks are anomalies of intent, making them invisible to both legacy signatures and passive posture scans. By executing defensive logic directly at the endpoint runtime using small language models (SLMs), it interprets user and agent intent to intercept risky or non-deterministic workflows at machine speed before a malicious action can fully manifest.

Zone 4: Intent-Aware Behavioral Analysis

Concept: Shifts endpoint control from passive file or process monitoring to active assessment of user and agent intent. This layer validates the behavioral risk profile of AI-driven interactions by correlating actions with their underlying intent. It ensures that autonomous agent activity and human-initiated commands are continuously evaluated against security policy in real-time, effectively neutralizing risks like malicious prompt hijacking, unauthorized agent orchestration, and anomalous workflow execution.

Operational Pillar: Deploys granular, intent-aware enforcement gates that provide real-time, behavioral guardrails rather than static blocking. By integrating surgical user coaching and inline friction directly into the interaction flow, this mechanism replaces binary, disruptive termination with adaptive, intent-aligned guidance that secures the workflow while maintaining productivity.

Anchors on intent and behavioral context, rather than just the file or software artifact. The core bet is that you cannot judge whether an AI-driven action is dangerous unless you understand the motivation behind it and the behavior it exhibits. This intent-anchored layer evaluates the risk of an action based entirely on its behavioral trajectory and alignment with authenticated user or agent intent, providing the control plane for mitigating autonomous misuse, shadow agent orchestration, and workflow manipulation. Rather than relying on rigid rules, next-generation architectures in this zone utilize local intent inference to issue pre-action verdicts at the point of interaction.

Zone 5: Data-Centric Enforcement

Concept: Sits at the data itself, classifying, tracing, and intercepting sensitive data movement at the moment of creation, use, or exfiltration across the endpoint, browser, and SaaS/API plane.

Operational Pillar

Anchors on the data object and its lineage to achieve convergence with DSPM and insider risk platforms, employing on-device AI classification at the endpoint edge to handle cert-pinned or SASE-bypassed traffic directly.

Sits at the data itself, classifying, tracing, and intercepting sensitive data movement at the moment of creation, use, or exfiltration across the endpoint, browser, and SaaS/API plane. The hypothesis: one cannot govern AI-era risk without understanding what data was touched, by whom, through what workflow, and whether that movement was authorized. Unlike Zones 1–4, which focus on software posture, attribution, behavior, or browser enforcement, Zone 5 anchors on the data object and its lineage, making it the natural control plane for insider risk, AI prompt leakage, shadow builder exfiltration, and supply chain data theft.

What distinguishes Zone 5 from legacy DLP: Classic DLP relied on static policy rules, perimeter chokepoints, and pattern matching, generating ~90% false-positive rates and high operational burden. Next-gen Zone 5 vendors replace policy brittleness with local AI classification, context-aware intent inference, and coaching-first enforcement that reduces noise and preserves workflow continuity.

Market Landscape

The Market Landscape: Decoupling ECP from Legacy EDR

The shift toward Endpoint Control and Prevention (ECP) signifies a fundamental decoupling of endpoint security from the constraints of legacy, process-tree-based detection models. As traditional security perimeters dissolve into dynamic, SaaS-mediated execution environments and autonomous agentic workflows, the market for defense is rapidly evolving. To address this complexity, the vendor landscape has begun to fragment, coalescing around three distinct strategic notable motions:

• Software Posture & Supply-Chain Governance: Treating the endpoint as an app store. Prioritizing the discovery and configuration risk-ranking of extensions, IDE plugins, and agentic toolchains rather than binary malware.

• Intent-Aware Observation & Semantic Defense: Moving beyond what a process did to why a process occurred. Ent and Origin HQ provide high-fidelity telemetry that maps prompt-to-action, establishing the forensic evidence chains required for governance.

• App-Layer Enforcement & Browser-Centric Control: Leveraging the browser as the primary control surface. Vendors like Neo Security and Cyberhaven move enforcement directly into the execution context (the browser or app), enabling surgical warn/block/guide interventions without the latency of kernel-level processing.

Emerging Players in Endpoint Control and Prevention (ECP)

Source: SACR Briefings 2026 (Sampling of Notable Vendors)

Agentless vs. Agent-Based Trade-offs

The deployment debate has persisted for decades, with many buyers rightfully wary of the systemic stability risks inherent in legacy kernel-integrated drivers, most notably the catastrophic Blue Screen of Death (BSOD) events. While the adoption of Extended Berkeley Packet Filter (eBPF) technology has significantly neutralized these driver-conflict anxieties, a strategic appetite for agentless architectures remains. The primary driver for agentless adoption is the elimination of net-new software footprints, allowing organizations to achieve specialized outcomes, such as advanced forensic depth, novel data correlation, or unique AI-driven inference, without the operational friction of traditional sensor management. Whether delivered via API integration or cloud-mediated browser plugins, endpoint harnesses, or agentless solutions, they might provide the surgical, complementary telemetry required to bridge functional gaps without duplicating the existing endpoint protection stack.

Agentless Deployment Performance Claims May be Misleading

The value proposition of agentless deployment includes that they ensure zero performance degradation, which isn’t entirely true. If a vendor solution hits customer API rate limits, or maybe query maximums, the solution may have significant gaps in data freshness and quality, leading to potentially poor alignment to detections or lack of the latest functionality releases. But on the endpoint, in some cases agentless solutions can significantly augment the current endpoint software’s use cases, either delivering new telemetry, data science or machine learning approaches of their own to a complementary endpoint protection tool. The primary drivers for adoption of agentless is for a customer to avoid net-new software or processes deployed, thereby optimizing endpoint battery life, and in the eyes of many buyers, the overall user experience.

Rapid Deployment and Scaling

Agentless solutions facilitate instantaneous deployment, typically requiring only a service account, an application API key, or standard credentials. Scalability is achieved seamlessly without necessitating reboots or software installations. Centralized control mechanisms of agentless solutions can sometimes simplify management, particularly for platforms utilizing differentiated or advanced reporting or offering analytic graph interfaces that incumbents aren’t providing out of the box. By minimizing the local attack surface, agentless options, if used to unify datasets and operations, help somewhat strengthen an organization’s security posture while maintaining compatibility and lowering the total cost of ownership (TCO). This aligns with a broader cybersecurity trend where specialized vendors integrate synergistically with existing infrastructures to deliver enhanced capabilities, such as data enrichment, advanced workflows, and deep analytical graphing.

Vendor Use Case Limitations and Dependency Issues

Agentless architectures present clear operational constraints. Chief among these are reduced context and visibility stemming from a lack of their own, controlled local file system access, drivers, or parsing engines, which can hinder deeper forensic investigations, delivery of new functionality, use cases or performance while they also remain fundamentally dependent on the capabilities and rate limits of provided APIs. Reliance on network connectivity introduces latency risks and potentially restricts real-time response actions when local remediation alternatives are absent. Organizations should implement a hybrid defense model with preference towards agent-based solutions where possible for the lowest latency of endpoint defensive measures. In some situations, customers of endpoint software might want agentless monitoring for high-performance locations or IoT environments that have limited processing capacity, where agentless may be more ideal, while deploying agent-based ECP solutions across critical endpoints that demand comprehensive file inspection and immediate, machine-speed containment and prevention capabilities.

Vendor Profiles

(by Primary ECP Expansion Zone)

Zone 1: Software Posture & Governance

Bloom Security

Vendor Profile

Bloom Security is an endpoint security company with Zone 1-3 coverage of the ECP market, delivering visibility, context, and control across the AI-native endpoint. It offers a hybrid AI-native endpoint security platform with selectable deployment options (agent and agentless), including a user-mode endpoint agent, supply-chain firewall, remediation, runtime guardrails, and policy enforcement. Bloom enables security teams to govern the modern workstation: what runs on it, how it’s configured, and whether it’s appropriate for the user and environment it operates in. Its approach is preventive, establishing governance at the workstation level before the conditions for incidents are created.

Bloom continuously discovers everything running across the fleet: AI agents, extensions, IDE plugins, MCP servers, packages, and CLI tools. It evaluates each asset and its configuration in context: who is running it, what data it can reach, and what else is installed alongside it. The same setup can be low-risk on one workstation and high-risk on another.

Bloom then acts on that context, enabling blocking of malicious or high-risk software before it installs, remediating dangerous configurations in place, setting runtime guardrails on what AI agents can access and execute, and enforcing policy continuously.
When enforcement happens, employees are told why, shown approved alternatives, and educated in the moment.

Applicable ECP Zones:

• Zone 1

• Zone 2

• Zone 3

Products/Services Overview

Bloom Platform: AI-native endpoint security platform that gives security teams complete visibility and control over the AI-native endpoint: what runs on it, how it’s configured, and how policy is enforced, without manual workflows or employee disruption. Bloom covers the full security cycle, from continuous discovery and contextual risk evaluation through direct enforcement and remediation, in a single platform.

Core Functions

• Discovery and Inventory: Continuously maps every AI agent, extension, plugin, MCP server, package, and CLI running across the fleet in real time

• Software Risk Analysis: Evaluates each through marketplace intelligence, static code analysis, and behavioral sandboxing to determine what it can do, what it can reach, and how it behaves

• Configuration and Context Analysis: Evaluates each against the workstation it runs on: the user’s role, their data access, and what else is installed alongside. The same setup can be low-risk on one workstation and high-risk on another.

• Install Prevention: Intercepts compromised or malicious components upstream via the Supply Chain Firewall before they reach the endpoint, and enforces installation policy continuously across the fleet

• Agentic Runtime Governance: Sets and enforces guardrails on what AI agents can access, execute, and connect to while running, scaling back over-permissioned configurations to safe defaults automatically

Use Cases and Pain Points Addressed

1. Endpoint Policy Enforcement: Bloom enforces precise, context-aware policy automatically, blocking risky installs and fixing configurations without manual review or employee friction.

2. AI Governance and Usage Control: Bloom enables security teams to govern how every AI tool, extension, and agent operates across the fleet: approved configurations, access boundaries, and usage policies per user and workstation

3. AI Supply Chain Security: Bloom’s Supply Chain Firewall monitors and intercepts marketplace traffic in real time, blocking compromised packages, malicious extensions, and backdoored MCP servers before they reach the endpoint. Bloom’s in-house research team surfaces findings before public disclosure, so customers are always protected first.

Key Takeaway / Recommendation

Bloom is the recommended first investment for enterprises securing the AI-native endpoint. Bloom’s preventive approach creates the foundation that detection and enforcement in higher zones depend on: establishing what runs across the fleet, how it’s configured, and whether it’s appropriate, before incidents occur. Bloom stands out by combining contextual risk evaluation, precise enforcement, and direct remediation in a single platform, with an approach to employee communication that addresses the productivity concern most security teams struggle to overcome.

Zone 2: Application Layer Enforcement

Neo Security

Vendor Profile

Neo Security is a Zone 1+2+3 Application Layer Enforcement vendor delivering lightweight, user-mode endpoint and browser security for AI-era threats. Neo offers a 3-stage control flow: Discover all software and define what should be allowed to run, fix any configuration and posture issues of that software and enforce deterministic runtime guardrails on it. Neo’s architecture combines a fast (~30-second) initial scanner with a persistent user-mode in-application sensor that instruments browser, office and IDE extensions as well as conducts agentic harness-level monitoring. Neo feeds an agentic backend that reverse-engineers the software characteristics and produces the expected “good” posture and behavior at runtime. Unlike kernel-mode EDR, Neo’s user-mode sensor carries zero kernel stability risk while still capturing high-fidelity application-layer telemetry covering extensions, plugins, libraries, MCP servers, skills, and LLM. Every interaction is attributed to the agent, model, or human that caused it, giving SecOps teams full prompt-to-action traceability for every agentic interaction. The critical architectural differentiator is flow-level enforcement: Neo starts with static and posture gates and then also monitors for potentially dangerous tool calls before they execute, not after the fact. This inline enforcement model at the point of API call or tool invocation, rather than at process creation, is specifically designed for the agentic workflow threat model, where machine-speed execution renders post-hook detection operationally irrelevant. Enforcement happens on device, and doesn’t require any sensitive data to leave the machine, given the sensor-based architecture. Neo requires no MDM, kernel module, or proxy, and supports IdP integration to complete the endpoint + tool + identity picture, rather than requiring it. Neo’s enforcement scope spans extensions, binaries, plugins, MCPs, skills, and LLMs, making it broadly applicable across shadow builder, agentic workflow, and developer toolchain use cases.

Applicable ECP Zones:

• Zone 1

• Zone 2

• Zone 3
  
  

Products/Services Overview

• Neo Scanner: Lightweight ~30-second initial scan of endpoint software inventory covering extensions, plugins, MCP servers, and AI toolchains

• User-Mode In-App Sensor: Persistent application-layer sensor for browser, IDE and office environments providing static monitoring and real-time telemetry and enforcement.

• Agentic Backend: A continually updated repository of agentic app analysis intelligence

• Flow-Level Enforcement: Inline enforcement layer that gates which extensions can be loaded, which binaries can be executed, and what tool calls, API invocations, and LLM interactions are allowed before execution.

• Agentic Copilot: A designated SOC, GRC and IT personas that fully automate any activity, research and policy creation over the Neo Platform.

Core Functions

• User-Mode Application Instrumentation: Instruments browser extensions, IDE plugins, and MCP servers at the user-mode application layer without kernel drivers or proxies, eliminating stability risk.

• Monitoring: Monitors all AI activity, including network activity like MCP calls, LLM communications, authentication and other API calls from the application itself (the harness, the browser, the IDE, etc).

• Flow-Level Pre-Completion Enforcement: Intercepts and evaluates tool calls, API invocations, and agentic actions before they complete, enabling inline block/warn/guide decisions at machine speed.

• Agentic Software Reverse-Engineering: Backend behavioral analysis engine models the risk profile of newly encountered extensions, plugins, and AI tools by reverse-engineering their runtime behavior.

• IdP-Only Deployment: Full enforcement capability requires only an IdP integration (no kernel module, MDM dependency, or proxy deployment), minimizing enterprise deployment friction. Other integrations are supported for enrichments but not required.

Use Cases and Pain Points Addressed

1. Agentic Conversation and Tool Call Interception: Full visibility into the agentic sessions. Neo blocks or gates dangerous tool invocations (e.g., a compromised MCP server calling a file exfiltration API) before the action is executed.

2. Browser, Office and IDE Extension Governance: Provides continuous monitoring and enforcement for extensions and plugins, surfacing over-permissioned or malicious components and blocking unauthorized actions.

3. Binary control: Control over which binaries should be able to run on the system, and which software should be installed to begin with (uninstall).

4. LLM and MCP Server Runtime Security: Monitors and gates interactions between enterprise users, AI models, and MCP-connected toolchains in real time.

Key Takeaway / Recommendation

Neo Security is the leading purpose-built Zone 1-3 enforcement-focused vendor, providing comprehensive control across the entire endpoint spanning binary and non-binary assets, as well as agentic and non-agentic software. It enables organizations to prioritize flow-level, pre-execution interception of tool calls and actions. Its IdP deployment model and user-mode architecture make it the lowest-friction path to genuine inline enforcement, not just posture visibility, for enterprises adopting AI agents and MCP-connected toolchains. CISOs should evaluate Neo as the primary enforcement layer in a Zone 1, Zone 2 and Zone 3 (enforcement-focused) stack. The key due diligence question is enforcement fidelity under high-volume agentic workloads: validate that Neo’s flow-level interception operates at the latency and throughput required for production agentic environments without introducing workflow-breaking delays.

Pluto Security

Vendor Profile

Pluto Security is a fully agentless AI workspace security platform occupying Zones 1-3 of the ECP market. Pluto delivers posture visibility into how employees actually use AI, contextual risk scoring that separates real danger from noise, and real-time enforcement that acts on those findings. Pluto governs three distinct layers spanning across AI tools employees use (ChatGPT, Claude, Copilot, etc.), the ecosystem surrounding those tools (npm packages, MCP servers, IDE plugins, browser extensions), and the artifacts employees build using those tools (custom apps, automated workflows, deployed agents). The platform deploys via API integrations and proprietary collectors that run through existing CrowdStrike, Defender, and MDM infrastructure, and claims to achieve approximately 30-minute deployment timelines even in large enterprises of 80,000+ employees. Pluto’s differentiation is structural. Most of the category secures one layer, the tool, the ecosystem, or the artifact, in isolation. Pluto not only connects context across all three layers but also delivers the visibility and governance to act on what that context reveals. A session that installs a package, calls an MCP server, ships an application, and runs a sequence of agent commands and prompts along the way is read as one event rather than three unrelated signals, with enforcement acting on the full picture rather than a single point. AI has turned employees into creators, wiring applications, agents, and workflows into production systems in an afternoon, often without a ticket, a review, or a security checkpoint. Pluto’s architectural bet is that the majority of AI-era endpoint risk is compositional: risk emerges from the combination of tools, permissions, and built artifacts, and that agentless monitoring of all three layers, paired with inline runtime hooks on coding agents, provides sufficient coverage to govern this risk without a local agent footprint.

Applicable ECP Zones:

• Zone 1

• Zone 2

• Zone 3

Products/Services Overview

• AI Tool Posture and Governance: Continuous discovery and policy enforcement for employee-facing AI applications (SaaS AI tools, local models, copilots);

• Ecosystem Posture and Governance: Discovery, risk-ranking, and enforcement for packages, MCP servers, IDE plugins, and browser extensions associated with AI workflows;

• Artifact Posture and Governance: Visibility, risk-ranking, and enforcement for apps, automated workflows, and AI agents built by employees using AI tools, the shadow builder output layer.

• Agentless Collector Infrastructure: Proprietary collectors operating through existing CrowdStrike, Defender, and MDM APIs, with an inline hook configuration on coding agents for runtime observability and enforcement where API coverage alone isn’t sufficient.

Core Functions

• Three-Layer Agentless Inventory: Simultaneously governs AI tools, their surrounding software ecosystems, and the artifacts (apps, agents, workflows) employees build with them.

• Rapid Enterprise Deployment: Achieves full fleet coverage via API integration in approximately 30 minutes for organizations of 80,000+ employees, with no reboots or agent installations.

• Shadow Builder Artifact Detection: Discovers and risk-ranks AI-assembled apps, scripts, and automated workflows deployed by non-IT employees, the artifact layer unique to Pluto’s scope.

• Policy-Based Access Control: Enforces allow/ask/deny policies on AI tool usage and ecosystem components via integration with existing MDM and IdP infrastructure.

• Continuous Risk-Ranking: Scores all three governance layers by permission scope, data access, behavioral signals, and upstream threat intelligence.

• Agent Runtime Observability and Governance: Continuously observes and governs AI agent execution, including prompts, commands, MCP servers, skills, tools, package installations, and network access, enabling context-aware policy enforcement.

Use Cases and Pain Points Addressed

1. AI Tool and Ecosystem Governance at Scale: Rapidly inventories and governs the full AI toolchain, tools, packages, and plugins across a large enterprise fleet without a deployment project.

2. Shadow Builder Artifact Risk Management: Surfaces AI-assembled apps, workflows, and deployed agents created by non-technical employees that introduce supply chain and data leakage risk.

3. Federated AI Adoption Risk Reduction: Gives security and GRC teams an auditable record of AI tool adoption patterns across business units for compliance, procurement, and risk reporting.

4. Govern AI Agent Behavior: Reduces the risk of AI agents performing unauthorized or unintended actions by enforcing runtime policies on agent execution, such as blocking an agent from collecting credentials and exfiltrating them to a remote location mid-session, helping organizations safely deploy AI into enterprise workflows.

Key Takeaway / Recommendation

Pluto Security offers the broadest agentless governance scope in Zone 1, covering tools, ecosystem, and artifacts. This makes Pluto one of the strongest choices for enterprises whose primary concern is the shadow builder problem: employees assembling and deploying AI-powered workflows without security review. The 30-minute deployment claim is compelling for organizations facing board or regulatory pressure to inventory AI tool risk quickly. Enforcement runs today across EDR, MDM, and IdP policy rules, plus inline coding-agent hooks for runtime control, with granular blocking down to a specific tool, skill, or MCP action rather than an all-or-nothing cutoff. Pluto’s applicability extends into Zone 2 and Zone 3, so this coverage doesn’t require pairing with a separate enforcement vendor. Organizations building toward full-spectrum coverage, including Zone 5 data-centric enforcement such as classification-driven data loss prevention, may still want to evaluate that layer to complete the coverage stack.

Glow

Vendor Profile

Glow is a cybersecurity startup operating in Zone 1 and 2 of the ECP market, currently valued at over ~$1 billion with backing from Sequoia, Redpoint, Index, and Cyberstarts, among the most prominent venture investors in enterprise security. Glow is building an agentic solution that maps all the software and applications running on the endpoint, including local apps, AI agents, MCPs, browser extensions, packages, and developer plugins. The platform uses AI agents to analyze risk and enforce policies, proactively deciding which software is allowed and which AI tool configurations need to be hardened. The software governance and app enforcement framing aligns Glow with Zone 1 and Zone 2, where the primary threat models involve supply chain threats like compromised plugins and extensions and malicious packages as well as agentic app misconfigurations and unvetted software issues that increase endpoint exposure. Glow’s $1B+ valuation, achieved while the company was still in stealth, signals extreme investor conviction in the problem space and the founding team’s pedigree, drawn from top-tier prior security startups and enterprises. Buyers should monitor Glow’s emergence closely given its capitalization level, which will enable aggressive go-to-market, potential acquisitions, and rapid product development. Glow is expected to compete directly with Bay and Pluto in the Zone 2 enforcement and identity-governance space.

Applicable ECP Zones:

• Zone 1

• Zone 2

Products/Services Overview

• Zone 1 Coverage: Software visibility, risk assessment and remediation of software plugins, packages, extensions, IDEs, and MCPs

• Zone 2 Coverage: Application-layer enforcement with the ability to block and remove software, review and remediate app configurations

Core Functions

• Inventory Reconciliation: Reconciles asset information across multiple data sources (EDR, MDM, IdP) and maps people to devices and software, enriched with information about vendor reputation, known issues, data privacy terms and other publicly available information

• AI Configuration Management: Discovers, analyzes and remediates configuration risks in agentic apps

• Supply Chain Exposure: Discovers, analyzes and remediates risks in the AI supply chain including MCPs, IDE plugins, browser extensions, and packages

• Software Control: Provides inline enforcement at the application and workflow layer, consistent with Zone 2 positioning.

Use Cases and Pain Points Addressed

1. Asset Management: Consolidates endpoint, user, and application data from existing sources into a single reconciled inventory, addressing the persistent challenge of incomplete or conflicting asset records across tools.

2. Software Control: Applies allow, audit, and block policies to installed software, packages, developer plugins, and browser extensions, with per-item risk assessment to identify and prevent execution of unauthorized or vulnerable code.

3. Safe AI Adoption: Provides visibility into AI tools, plugins and MCP servers, and enforces configuration and usage policy to manage AI risk at the endpoint layer.

Key Takeaway / Recommendation

Glow is a high-signal watch-list vendor for CISOs and security architects evaluating the Zone 1 and Zone 2 enforcement space. Its $1B+ valuation and top-tier VC backing suggest a founding team and product thesis that sophisticated investors consider category-defining. With their impending launch, buyers should request briefings and demos to learn more about its current capabilities and roadmap.SACR will update this profile with full product details upon demo completion. In the interim, Glow should be treated as a strong potential competitor to Bay and Pluto, particularly given its software and application governance focus.

NeuralTrust

Vendor Profile

NeuralTrust is an EU-based agentic runtime security platform that raised a $20M seed round in June 2026, the largest EU cybersecurity seed to date, to build centralized discovery and governance of AI agents from reasoning and planning through to action execution. NeuralTrust occupies Zone 2 of the ECP market as an AI infrastructure control plane rather than a traditional OS-level endpoint agent, operating through TrustGate, its agent gateway, which serves as the connectivity and enforcement layer between software, agents, tools, and models (API gateway, MCP proxy, and SDK integration) to enforce security policy mid-flow. The platform comprises four products: TrustGate (agent gateway), TrustGuard (runtime security), TrustLens (agent discovery and posture), and TrustTest (red teaming), delivering capabilities such as prompt injection detection, data leakage prevention, and runtime policy enforcement, and has been recognized by analyst firms as an enterprise platform for securing AI agents. NeuralTrust’s architecture is fundamentally different from OS/browser-layer Zone 2 vendors like Neo: it is designed for organizations building and deploying AI agents in production, instrumenting the agent reasoning and planning layer rather than the endpoint execution layer. This makes NeuralTrust most relevant to engineering and platform security teams managing internal AI agent deployments, AI application developers, and enterprises requiring governance of AI agents that operate autonomously across cloud services and APIs. The EU origin is relevant for buyers with data residency and GDPR requirements, as NeuralTrust’s architecture is designed with European compliance standards in mind.

Applicable ECP Zones:

• Zone 2

• Zone 3

Products/Services Overview

• TrustGate (Agent Gateway): Connectivity and enforcement layer between software, agents, tools, and models; manages API gateway, MCP proxy, and SDK integration.

• TrustGuard (Runtime Security): Agent-to-agent flow governance, tool authorization, indirect prompt injection protection, and runtime DLP.

• TrustLens (Agent Discovery and Posture): Centralized discovery, cataloging, and security posture management for agents across the enterprise.

• TrustTest (Red Teaming): Security testing, validation, and red teaming for AI agents.

Core Functions

• TrustGate Gateway Enforcement: Serves as the central connectivity and enforcement layer mid-flow, executing API gateway, MCP proxy, and SDK integration controls across software, tools, and models.

• TrustGuard Agent Security Scope: Enforces advanced runtime policies that govern the entire agent reasoning layer, including tool authorization, autonomous agent-to-agent flow governance, and runtime data leakage prevention.

• Indirect Prompt Injection Prevention: Leverages TrustGuard to deliver real-time detection and blocking of adversarial inputs and indirect prompt injections, preventing agent hijacking during execution.

• TrustLens Centralized Discovery and Posture: Catalogues and inventories all AI agents across the enterprise, managing security posture from development through production deployment.

• TrustTest Agent Red Teaming: Conducts specialized security validation, continuous testing, and adversarial red teaming focused specifically on autonomous agent action execution and planning paths.

Use Cases and Pain Points Addressed

1. Production AI Agent Runtime Security: Governs AI agents deployed in production, customer service bots, internal copilots, and autonomous workflow agents, enforcing security policy on every reasoning step and tool call.

2. Prompt Injection Attack Prevention: Detects and blocks adversarial prompt injection attacks targeting enterprise AI agents, preventing attackers from hijacking agent behavior via crafted inputs or via poisoned content and tool outputs the agent consumes (indirect prompt injection), blocked at enforcement points

3. GDPR-Compliant AI Governance: Provides EU-resident AI agent governance with data residency controls, audit logging, and compliance reporting aligned to GDPR and emerging EU AI Act requirements.

Key Takeaway / Recommendation

NeuralTrust is the leading purpose-built Zone 2 option for organizations managing production AI agent deployments and requiring governance at the reasoning and planning layer which is above and beyond what OS and browser-layer endpoint tools can reach. Its $20M seed backing signal offers a strong market validation for the platform thesis. CISOs should position NeuralTrust as the AI infrastructure security layer complementary to endpoint-focused Zone 2 vendors like Neo. Neo governs the human user and endpoint tool layer, while NeuralTrust governs the autonomous AI agent and API layer. For organizations with GDPR or EU AI Act compliance requirements, NeuralTrust’s EU-native architecture is a significant procurement advantage. CISO’s should evaluate NeuralTrust early in any production AI agent deployment programs; runtime security is significantly easier to instrument before agents go to production than after.

Zone 3: Agent Runtime Observability (AI-EDR)

Origin (OriginHQ)

Vendor Profile

Origin (operating as OriginHQ / Prelude) is a Zone 3 Agent Runtime Observability vendor delivering probabilistic endpoint defense through a proprietary user-mode agent, patented CPU branch and instruction telemetry, and a local graph database that stores per-endpoint behavioral baselines. Origin’s core architectural thesis is that novel AI-era attacks are anomalies of intent where they are invisible to legacy signatures and passive posture scans but detectable as statistical deviations from an established behavioral baseline built from prompt-to-action traces.

The Origin platform watches every AI-agent interaction, cloud-connected or local-only, on the endpoint, including prompts, tool calls, shell commands, file writes, and network activity, across laptops, build servers, and CI/CD runners. Origin’s local TLS-intercepting proxy and OTEL collector achieve hook coverage without kernel-mode instrumentation, and its local graph database ensures that behavioral inference operates on-device, eliminating cloud-pipeline latency and cost at agentic telemetry volumes. Origin positions as the next layer stacked on top of incumbent EDR platform add-ons in the user behavioral analysis and agentic endpoint observability space.

Applicable ECP Zones:

• Zone 3

Products/Services Overview

• User-Mode Endpoint Agent: Proprietary user-mode agent with patented CPU branch/instruction telemetry and local TLS-intercepting proxy; no kernel driver required. Also surfaces shadow AI locally, including agent hardnesses, IDEs, and browser-based AI, with no cloud connection or extra installation required.

• Local Graph Database: Per-endpoint graph database storing behavioral baselines and prompt-to-action traces for on-device inference

• Agentic Observability Platform: Real-time monitoring of AI agent interactions, prompts, tool calls, shell commands, file and network activity, across laptops, build servers, and CI runners

• Probabilistic Behavioral Defense: An anomaly detection engine using local AI models and Bayesian behavioral baselines to detect intent-level deviations at machine speed

• AI Observability Ontology: Built-in relational model of agent activity that correlates telemetry into the context graph and prompt-to-action trace, turning disconnected event logs into one connected investigation narrative.

Core Functions

• Patented CPU-Level Telemetry: Captures CPU branch and instruction telemetry to build high-fidelity behavioral baselines without kernel-mode instrumentation, achieving coverage via a user-mode agent.

• Local TLS Interception: Intercepts TLS-encrypted application traffic and OTEL locally on the endpoint to capture full AI model API call content including prompts, responses, and tool invocations.

• Per-Endpoint Local Graph Database: Maintains a behavioral graph database on each endpoint, enabling on-device probabilistic inference without requiring cloud-backend latency.

• Prompt-to-Action Lineage: Establishes absolute traceability from natural language prompts through tool invocations to downstream system actions, providing forensic evidence chains for governance and incident response.

• On-Device Behavioral Inference: Executes anomaly detection locally using small language models and graph-based baselines, addressing agentic telemetry volume without cloud-pipeline cost.

Use Cases and Pain Points Addressed

1. Agentic Workflow Observability and Forensics: Provides complete prompt-to-action traceability for AI agent interactions across the enterprise, enabling post-incident reconstruction and governance.

2. Intent-Level Anomaly Detection: Detects AI-era attacks that bypass signature-based detection by identifying statistical deviations from established behavioral baselines at the intent layer.

3. CI/CD and Build Server Agentic Security: Extends behavioral monitoring and enforcement to build servers and CI/CD runners where AI agents operate outside traditional endpoint security perimeters.

Key Takeaway / Recommendation

Origin is the most technically differentiated Zone 3 vendor in the market, with its patented CPU telemetry, local graph database, and endpoint-centric architecture representing a genuine architectural moat against cloud-dependent incumbent EDR platforms. For CISOs managing large developer populations and AI agent deployments in production environments, particularly those running CI/CD pipelines and build infrastructure, Origin provides observability and forensic depth that no incumbent EDR platform currently matches. Buyers should evaluate Origin in environments where forensic evidence quality and agentic observability depth are primary buying criteria, rather than fast time-to-first-detection on commodity threats.

Zone 4: Intent-Aware Behavioral Analysis

Ent

Vendor Profile

Ent is a Zone 4 Intent-Aware Behavioral Analysis vendor that deploys a proprietary application-layer endpoint agent to build multimodal behavioral baselines of user and agent intent, then enforces policy through real-time pop-up interventions that block, warn, or redirect risky actions before completion. Unlike kernel-mode or network-layer EDR, Ent’s agent mints its own telemetry stream, capturing clicks, window focus, clipboard content, file activity, and screen context, and processes this multimodal data using on-device AI models (the “edge” inference architecture). Ent’s policy language, EntLang, enables security teams to express behavioral intent policies in structured natural language that the on-device model evaluates against the Bayesian behavioral baseline in real time. The Bayesian baseline uses a 90-day decay model, ensuring that behavioral norms adapt to legitimate changes in user workflows without requiring manual policy updates. Ent’s user-facing intervention model, real-time pop-ups that block, warn, redirect, alert, or block, where supported, is designed to replace the binary allow/block paradigm with a graduated, coaching-first enforcement experience that reduces friction for legitimate users while stopping risky behaviors at machine speed. Like Origin, Ent pushes inference to the device to handle agentic-volume telemetry without cloud-pipeline cost, which is the key architectural asymmetry against CrowdStrike’s AIDR network-dependent model.

Applicable ECP Zones:

• Zone 3

• Zone 4

• Zone 5

Products/Services Overview

• Application-Layer Endpoint Agent: Proprietary agent that captures multimodal telemetry (clicks, focus, clipboard, screenshots) for user and agent behavioral analysis

• Edge AI Inference Engine: On-device multimodal AI model for real-time intent evaluation against behavioral baselines

• EntLang Policy Engine: Structured natural language policy language enabling security teams to express intent-based behavioral and organizational rules

• Bayesian Behavioral Baseline: Adaptive behavioral model with 90-day decay that baselines both user and agent behavior and detects anomalies

• Real-Time Intervention System: Pop-up enforcement layer delivering block/warn/redirect/block interventions inline within the active workflow

• Data Sovereignty: Data stays within the customer boundary where the Ent platform is self-hosted and managed by the customer, delivering privacy and trust by design

Core Functions

• Multimodal Telemetry Capture: Mints proprietary telemetry including clicks, window focus, clipboard content, and screen context to build high-fidelity behavioral context for both users and AI agents operating on the endpoint.

• On-Device Intent Inference: Executes behavioral intent analysis locally using edge AI models, eliminating cloud-pipeline latency and cost while maintaining privacy for sensitive telemetry streams.

• Bayesian Behavioral Baselines: Maintains adaptive 90-day behavioral baselines for users and agents using Bayesian methods, automatically adjusting to legitimate workflow changes without manual tuning.

• EntLang Policy Authoring: Enables security teams to express behavioral intent policies in structured natural language, reducing the operational expertise required to configure intent-aware enforcement rules.

• Graduated Enforcement Interventions: Delivers real-time block/warn/redirect interventions via user-facing pop-ups, in addition to block and alert where supported, replacing binary allow/block with surgical, context-appropriate enforcement that coaches users rather than breaking workflows.

Use Cases and Pain Points Addressed

1. User and Agent Intent Verification: Distinguishes between legitimate user-directed AI actions and malicious or anomalous agent behaviors using multimodal behavioral context.

2. Insider Risk and Data Exfiltration Prevention: Detects and intercepts risky data movement and exfiltration behaviors by users and AI tools using clipboard, screen activity, and focus telemetry.

3. Shadow Builder and AI Tool Misuse Governance: Monitors and enforces policy on user and agent interactions with AI tools and applications, flagging and intercepting misuse patterns before data or credential exfiltration completes.

Key Takeaway / Recommendation

Ent is the strongest Zone 4 option for organizations prioritizing user-facing coaching enforcement and insider risk governance alongside agentic behavioral analysis. Its multimodal telemetry approach, capturing the full richness of user and agent interaction context, not just process and network signals, provides a behavioral depth that traditional EDR and network-layer tools cannot replicate. The EntLang policy language and Bayesian adaptive baselines significantly reduce the operational overhead of intent-aware enforcement compared to rule-based alternatives. CISOs should note that Ent overlaps with Bold Security on insider risk positioning; the differentiation is that Ent focuses on intent and behavior while Bold focuses on data payload and lineage, a complementary rather than competing stack in mature deployments. Origin provides the deepest forensic and developer pipeline observability, while Ent provides the strongest user-facing enforcement and insider risk governance.

Harmonic Security

Vendor Profile

Harmonic Security is a Zone 4 Intent-Aware Behavioral Analysis vendor delivering AI-native sensitive data classification and governance purpose-built for the era of enterprise AI tool adoption. Unlike legacy DLP platforms that rely on static pattern-matching rules, Harmonic uses AI-powered classification to identify sensitive data flowing into and out of AI applications, SaaS platforms, and collaboration tools in real time, at the point of user interaction, before data movement completes. Harmonic’s primary architectural bet is that the GenAI data leakage problem cannot be solved with rule-based DLP because the data types, destinations, and interaction patterns are too dynamic and context-dependent for static policies to govern. Its classification engine understands business context and not just PII and PCI patterns, enabling it to detect sensitive data submissions to AI tools that traditional DLP misclassifies as benign. Harmonic is designed to extend, rather than replace, existing endpoint and insider risk stacks, providing the AI tool governance layer that legacy DLP and Code42-style insider risk platforms were not built to address.

Applicable ECP Zones:

• Zone 2

• Zone 4

• Zone 5

Products/Services Overview

• AI-Native Data Classification Engine: Real-time classification of sensitive data flowing into AI applications, SaaS platforms, and collaboration tools using AI models tuned for business-context data types

• AI Tool Data Flow Monitoring: Continuous monitoring and enforcement for data submitted to or extracted from GenAI tools (ChatGPT, Claude, Copilot, Gemini) via endpoint and browser interactions

• Shadow AI Data Governance: Discovery and governance of unauthorized AI tool usage and the data flows they generate, providing visibility into unapproved GenAI adoption across the enterprise

• Workflow-Integrated Enforcement: Inline coaching and policy enforcement delivered within the active user workflow rather than via disruptive hard blocks

• Remote MCP Gateway: Governs MCP servers and agent tool calls inline, blocking dangerous tool-calls or risky data sharing mid-flow without killing the session.

Core Functions

• Business-Context AI Classification: Classifies sensitive data submissions to AI tools using AI models that understand business-context categories (financial data, IP, HR records, source code) beyond standard PII/PCI/PHI pattern matching.

• Real-Time AI Tool Interaction Governance: Monitors and enforces policy on data flowing into GenAI tools at the point of submission, prompt text, file uploads, clipboard paste, before the data reaches the model.

• Shadow AI Discovery and Risk Ranking: Identifies unauthorized AI tool usage across the enterprise fleet, ranking data exposure risk by tool, user, and data type to prioritize governance interventions.

• Inline User Coaching: Delivers contextual coaching and policy guidance to users at the moment of a risky AI tool interaction, reducing friction for legitimate use while intercepting genuine policy violations.

• SIEM/SOAR Integration: Feeds AI tool data governance findings into existing SOC workflows via SIEM and SOAR integrations, enabling correlation of AI-era data movement events with identity and endpoint telemetry.

Use Cases and Pain Points Addressed

1. GenAI Prompt Leakage Prevention: Detects and intercepts sensitive business data submitted to AI tools in AI prompts, source code, financial reports, customer PII, HR data before it reaches the model and potentially leaves the organization’s control.

2. Shadow AI Data Exposure Governance: Provides security and GRC teams with visibility into which employees are using which AI tools and what categories of sensitive data are flowing through those interactions, enabling risk-based governance without blanket blocking.

3. AI Tool DLP Gap Coverage: Closes the data governance gap that legacy DLP platforms leave open when employees interact with AI tools via browser, which is a channel that network-layer DLP and proxy-based tools struggle to inspect with sufficient business context.

Key Takeaway / Recommendation

Harmonic Security spans Zones 2, 4, and 5 natively, anchored in Zone 4 as its primary zone, while also providing coverage for Zones 1 and 3 through shadow AI discovery and prompt-to-action visibility. This native multi-zone reach makes it the strongest option for organizations whose primary data governance gap is sensitive data flowing into GenAI tools and AI-assisted workflows, which represent a risk that legacy DLP platforms and traditional insider risk tools were not architected to address. Its AI-native classification engine provides the business-context awareness required to govern AI tool interactions without generating the false-positive rates that make legacy DLP operationally unsustainable. CISOs should position Harmonic as the AI tool data governance layer within an existing Zone 4 stack. The key evaluation question is classification accuracy for the organization’s specific sensitive data types in AI tool interaction contexts; request a proof-of-concept with representative prompt and file submission scenarios to validate detection fidelity before broad deployment.

Zone 5: Data-Centric Enforcement

Bold Security

Vendor Profile

Bold Security is a Zone 4/5 Intent-Aware Behavioral Analysis and Data-Centric Enforcement vendor that deploys a modular endpoint agent and an agent-deployed browser extension (no proxy required) to classify sensitive data locally on the device using small language AI models, delivering pre-action verdicts and inline coaching at creation, download, clipboard moments and other ingress and egress moments. Bold’s core architectural bet is that effective data protection in the AI era requires on-device AI classification, not network-layer proxies or cloud-backend DLP rules, because a growing proportion of sensitive data movement occurs within cert-pinned AI applications (ChatGPT, Claude, Copilot) that network proxies cannot inspect. Bold’s classification engine extends beyond traditional PII and PCI categories into business-context categories: financial reports, IT/security data, AI application outputs, and custom organizational data types. The platform provides stacked data lineage, human risk scoring, and AI risk scoring in a unified view, and has been validated at 100,000-endpoint enterprise scale. Bold’s on-device AI classification is the primary architectural differentiator, a distinction that becomes increasingly material as enterprise AI tool adoption grows and proxy-bypassed data movement becomes the norm.

Applicable ECP Zones:

• Zone 4

• Zone 5

Products/Services Overview

• Modular Endpoint Agent: Lightweight endpoint agent providing on-device AI data classification and enforcement at creation, download, and clipboard interaction points

• Agent-Deployed Browser Extension: Proxy-free browser extension for in-browser data movement monitoring and enforcement, deployed via the endpoint agent without proxy infrastructure

• On-Device AI Classification Engine: Small AI models running locally that classify data beyond PII/PCI into business-context categories at enterprise scale

• Data Lineage and Risk Scoring: Unified data lineage view with stacked human risk, AI risk, and data risk scoring for investigation and governance

• Inline Coaching and Justification Prompts: User-facing enforcement delivering pre-action verdicts with coaching and exception justification workflows rather than hard blocks

Core Functions

• On-Device AI Data Classification: Classifies sensitive data locally using small AI models at creation, download, and clipboard moments, covering business-context categories beyond standard PII/PCI/PHI.

• Cert-Pinned AI App Traffic Inspection: Inspects data movement within cert-pinned AI applications (ChatGPT, Claude, Copilot) that network proxies cannot reach, closing a critical gap in legacy DLP architectures.

• Pre-Action Data Verdict Enforcement: Delivers block/warn/coach verdicts before data movement completes, at the moment of clipboard paste, file download, or prompt submission rather than detecting exfiltration post-hoc.

• Stacked Risk Scoring: Provides unified human risk, AI risk, and data lineage scoring in a single dashboard, enabling security teams to correlate data movement risk with user behavioral and AI tool usage context.

• Inline User Coaching: Delivers contextual coaching and justification prompts to users at the point of risky data interaction, reducing false positives and building security-aware behavior rather than blocking workflows.

Use Cases and Pain Points Addressed

1. AI Application Data Leakage Prevention: Prevents sensitive data from being pasted into or extracted from cert-pinned AI applications (ChatGPT, Claude, Copilot) that bypass network-layer DLP.

2. Shadow Builder Data Exfiltration Prevention: Detects and intercepts sensitive data movement within AI-assembled workflows and shadow builder toolchains before exfiltration completes.

3. AI Risk and Human Risk Correlation for Investigations: Provides stacked data lineage with human risk and AI risk scoring to accelerate incident investigation and insider threat analysis.

Key Takeaway / Recommendation

Bold Security is a leading purpose-built Zone 4/5 option targeting cert-pinned AI application traffic missed by network-layer DLP. Operating at a validated 100,000-endpoint scale, its proxy-free architecture uses a modular agent and browser extension to classify data locally using small AI models. Classification goes beyond PII to business-context categories and user policies, providing stacked data lineage, human risk, and AI risk. Bold delivers pre-action verdicts at creation, download, and clipboard moments, utilizing inline coaching or justification prompts. CISOs should position Bold as an on-device enforcement layer complementary to network CASB/SWG tools, not a replacement.

Cyberhaven

Vendor Profile

Cyberhaven is a data lineage and browser-centric Zone 5 governance platform that tracks the origin, movement, and destination of sensitive data across endpoint, browser, SaaS, and cloud environments, providing high-fidelity evidence chains of user data interactions. Unlike traditional DLP tools that classify data by content pattern at a point in time, Cyberhaven builds a continuous lineage graph that follows data from its point of origin- a document created, a record copied from a database, a file downloaded from a cloud service- through every subsequent interaction, transformation, and transfer. This lineage model enables security teams to answer the forensic question that matters most in AI-era investigations. Cyberhaven’s browser-centric architecture captures SaaS and web application data interactions that endpoint-only agents miss, making it particularly relevant for organizations where the majority of sensitive data movement occurs within browsers interacting with cloud-based AI tools, SaaS platforms, and collaboration applications with on-device AI classification as the primary architectural differentiator.

Applicable ECP Zones:

• Zone 5

Products/Services Overview

• Data Lineage Platform: Continuous tracking of sensitive data origin, movement, and destination across endpoint, browser, SaaS, and cloud environments

• Browser-Centric Governance: Browser extension-based data movement monitoring providing full visibility into SaaS application and web-based data interactions

• Data Movement Evidence Chains: High-fidelity forensic evidence chains of user data interactions for incident investigation, governance, and compliance reporting

• AI Tool Data Flow Monitoring: Visibility into data flows into and out of AI applications accessed via the browser

Core Functions

• Continuous Data Lineage Tracking: Follows sensitive data from its origin through every subsequent interaction, transformation, and transfer across endpoint, browser, and cloud, building a persistent lineage graph.

• Browser Data Movement Capture: Instruments the browser to capture the full richness of SaaS and web application data interactions, including copy-paste, upload, download, and AI tool prompt submissions.

• Forensic Evidence Chain Generation: Produces investigation-grade evidence chains that answer the origin, path, and destination questions for any sensitive data movement event.

• AI Tool Data Flow Visibility: Monitors data flowing into and out of browser-accessed AI tools (ChatGPT, Copilot, Gemini), providing governance coverage for AI-mediated data movement.

• Cross-Environment Lineage Correlation: Correlates data movement events across endpoint, browser, SaaS, and cloud to provide a unified view of sensitive data flows across the enterprise.

Use Cases and Pain Points Addressed

1. Insider Threat and Exfiltration Forensics: Provides complete data lineage for forensic investigation of insider threat and exfiltration incidents, answering origin, path, and destination questions that traditional DLP cannot.

2. AI Tool Data Governance: Monitors and governs sensitive data flowing into browser-accessed AI tools, detecting prompt leakage and unauthorized AI-mediated data movement.

3. Departing Employee Data Exfiltration Detection: Detects and investigates data exfiltration by departing employees by tracking sensitive data movement patterns in the period leading up to departure.

Key Takeaway / Recommendation

Cyberhaven is a strong Zone 5 data lineage platform, offering the most mature and proven browser-centric data governance capability in the market. For organizations prioritizing forensic evidence quality, insider threat investigation depth, and AI tool data flow governance, Cyberhaven provides capabilities that legacy DLP and most next-gen alternatives do not match on lineage depth. CISOs should evaluate Cyberhaven as the data lineage and evidence chain foundation of their Zone 5 stack. The key consideration is that Cyberhaven’s primary strength is lineage and forensics rather than real-time prevention; organizations requiring pre-action enforcement should layer Cyberhaven with a prevention-first enforcement vendor rather than treating it as a standalone DLP replacement.

Multi-Zone: Converged Endpoint Control and Prevention Platforms (ECPP)

Note: Vendors must be in 4 or more zones

Bay

Vendor Profile

Bay is an agentless, prevention-first endpoint AI security platform operating in Zone 1 through 4 of the ECP market. Its capabilities span four layers: discovery and posture management, supply chain security and application control, visibility into agentic activity, and real-time, session-aware enforcement. At the discovery layer, Bay builds a contextual entity graph of the AI agents, browser extensions, IDE extensions, MCP servers, skills, plugins, connectors, models, and packages running across the fleet, mapping permissions, entitlements, personal versus corporate account use, and toxic tool combinations, and raising posture findings. At the supply chain layer, it extracts malicious indicators and vulnerabilities from the components agents load, with centralized approve, block, and uninstall controls and application allowlisting. At the activity layer, it records agent actions end to end, from prompt to tool call to system action, for investigation and audit. Then it analyzes the agentic activities to author out-of-the-box intent-aware policies that fit any workflows and departments within the organization.

A key differentiator for Bay relative to some Zone 1 players is that it does not stop at discovery or posture reporting: enforcement is endpoint-resident and executed locally as each agent action is invoked, evaluating that action in the context of the session rather than in isolation. Bay states that once a session touches sensitive data or credentials, it closes the outbound exfiltration routes for the remainder of that session, blocking the sequence rather than only flagging it, and that on-device enforcement runs at sub-4ms p95 latency with no cloud round trip. The platform is designed to ride existing EDR/MDM and orchestration rails such as Intune and CrowdStrike, deploying an ephemeral collector rather than a net-new persistent agent, and to run across Windows, Mac, and Linux endpoints, cloud endpoints and containers. Bay frames its value around stopping incidents before they occur across the agentic supply chain rather than generating a risk report for human review, and positions itself as distinct both from pure visibility players and from EDR, which observes processes and network connections but, in Bay’s account, cannot distinguish AI activity from human activity. This makes Bay relevant to organizations seeking discovery, application control, and active enforcement of agentic activity in a single agentless deployment.

Applicable ECP Zones:

• Zone 1

• Zone 2

• Zone 3

• Zone 4

Products/Services Overview

• AI Tool and Extension Discovery: Agentless continuous discovery of AI agents, extensions, MCP servers, skills, plugins, models and packages across the enterprise fleet, mapped into a contextual entity graph with risk scoring, surfacing permission overreach, personal-account use, and toxic tool combinations as posture findings.

• Supply Chain Security and Application Control: Malicious-indicator and vulnerability extraction across browser extensions, IDE extensions, MCP servers, packages, skills, and models, with centralized approve, block, and uninstall, and application allowlisting.

• Credential Exposure Remediation: Active identification and remediation of exposed secrets, API keys, and credentials embedded in extension configurations and agentic toolchains

• Real-Time and Session-Aware Enforcement: Endpoint-resident Allow/Ask/Deny policy enforcement via existing EDR/MDM sensors that evaluates each action locally and in session context, and closes exfiltration routes once a session touches sensitive data or credentials.

• Agentic Workflow Visibility & Forensics: Record of agent actions with full context, human versus autonomous, prompt-to-action chain, and timestamps, used for investigation, audit, and policy tailoring.

Core Functions

• Agentless Prevention-First Posture: Discovers and risk-ranks AI tools, extensions, and agentic workflows with a patent-pending ephemeral collector via EDR/MDM with a focus on preventing incidents rather than reporting them post-hoc.

• Supply Chain and Application Control: Extracts malicious indicators and vulnerabilities from the components agents load, flags risky items, blocks or uninstalls them, and controls which applications and packages are permitted to run.

• Session-Aware Policy: Evaluates each action against session state rather than in isolation, closing exfiltration routes for the remainder of a session once credentials or sensitive data are touched.

• Credential and Secret Exposure Detection: Actively identifies secrets, API keys, OAuth tokens, and credentials exposed in extension configurations, agentic tool settings, and workflow artifacts.

• Real-Time Policy Enforcement: Enforces Allow/Ask/Deny on the device as each AI tool action is invoked, evaluating the action across files, network destinations, credential access, MCP tool calls, and shell capabilities, and combining deterministic rules with behavioral context. Bay reports sub-4ms p95 latency for on-device decisions. Agentic Workflow Governance: Captures the prompt-to-tool-call-to-system-action chain for each agent, providing a single point for forensics and the behavioral basis for tailoring policy per identity-provider group.

• Credential Remediation Workflows: Provides guided remediation workflows for exposed credentials, including rotation recommendations and integration with secrets management platforms.

Use Cases and Pain Points Addressed

1. Credential Exposure in Agentic Toolchains: Identifies API keys, tokens, and secrets embedded in agentic configurations and extension permissions before attackers can harvest them.

2. Prevention-First AI Tool Governance: Discovers unapproved AI tools and agentic workflows and enforces real-time allow/ask/deny policy on the device..

3. Shadow AI and Shadow Builder Risk Reduction: Surfaces unauthorized AI tool deployments and agentic workflow configurations created by non-IT employees, enabling governance without blocking legitimate productivity.

4. Agentic Supply Chain Exposure: Identifies malicious or vulnerable extensions, MCP servers, packages, and models, and blocks or removes them from a central point.

Key Takeaway / Recommendation

Bay is the strongest Zone 1 option for organizations whose primary concern is credential exposure within the non-binary software and agentic toolchain layer, a specific and underserved risk that pure posture scanners address only partially. The prevention-first positioning and credential remediation capability make Bay a meaningful complement or alternative to Bloom/Pluto depending on whether credential hygiene or supply chain visibility is the primary buying criterion. CISOs should evaluate Bay alongside their existing secrets management and IdP posture (e.g., CyberArk, HashiCorp Vault integrations) to assess whether Bay’s remediation workflows integrate cleanly with incumbent tooling. As with all Zone 1 agentless players, enforcement depth for novel AI tool installations requires a complementary Zone 2 enforcement layer for complete prevention coverage.

Bay is a strong option for organizations that want more than posture reporting at the agentic endpoint layer. A principal distinction from posture-only players is an endpoint-resident, session-aware enforcement engine that acts on each AI tool action in real time, a layer those tools do not provide, and one that Bay argues EDR cannot deliver because EDR does not distinguish AI activity from human activity. That said, discovery and posture overlap materially with the incumbent scanners, so the enforcement layer is where Bay should be evaluated most closely. Two points bear on any assessment. First, prevention depends on Bay’s on-device enforcement component; a collector-only deployment runs in detection mode, so buyers should confirm which mode a given deployment is in. Second, several of the breadth claims, including scanning of skills, plugins, and models and the stated latency and cross-platform parity, are vendor-reported and would benefit from validation in a POV. CISOs evaluating Bay should scope a proof of value around the enforcement engine specifically, and confirm how its posture and activity findings integrate with existing identity, EDR, and secrets tooling.

CrowdStrike

Vendor Profile

CrowdStrike occupies a uniquely distinct position in the ECP market: platform convergence via acquisition and organic expansion, attempting to cover all five ECP zones simultaneously from its Falcon sensor base. Rather than a pure-play ECP startup, CrowdStrike is the incumbent EDR market leader extending its platform into the AI-era through Falcon AIDR and Falcon Secure Access (Seraphic Security acquisition of browser-layer enforcement). Falcon AIDR delivers prompt and LLM-response layer visibility, threat detection, data protection, and automated response across endpoints, SaaS, and cloud environments via Falcon Sensor integration, browser extensions, AI gateway integrations, SDK, and an MCP Proxy.

CrowdStrike’s two AIDR core use cases are workforce AI adoption governance (monitoring and controlling employee use of GenAI tools) and AI application runtime security (runtime guardrails for engineers building agents and agentic workloads). CrowdStrike’s strategic position is distribution and platform convergence: it can deliver ECP capabilities at attach rates no startup can match by embedding them into the Falcon platform already deployed across millions of endpoints. The structural vulnerability startups are exploiting is AIDR’s network-connection dependency; its prompt-layer telemetry requires network connectivity rather than operating at the local on-device inference layer.

Applicable ECP Zones:

• All Zones

Products/Services Overview

• Falcon Insight XDR: Unified endpoint detection and response (EDR) and extended detection and response (XDR) with enterprise-wide visibility to automatically detect adversary activity and respond across endpoints, identities, and cloud workloads.

• Falcon AIDR: Prompt- and LLM response layer AI visibility, governance, detection and response module delivering visibility, threat detection, data protection, and response spanning endpoints, SaaS, and cloud environments via Falcon Sensor integration, browser extensions, AI gateway integrations, SDK, and an MCP Proxy.

• Falcon Secure Access: Browser-layer enforcement capability acquired to extend Falcon into Zone 2 / App-Layer Enforcement

• Falcon Next-Gen SIEM: Cloud-native security data platform that unifies third-party log ingestion and long-term storage with CrowdStrike’s native telemetry and AIDR findings for AI-powered detection, investigation and response.

• Charlotte AI: CrowdStrike’s generative AI assistant embedded across the Falcon platform for analyst augmentation and accelerated investigation

Core Functions

• Prompt-and LLM Response Layer Visibility and Governance: Captures full prompt content (including secrets) via Falcon Sensor integrations, browser extension and API/SDK/gateway integration; maps relationships between users, prompts, models, agents, and MCP servers in a unified visibility dashboard.

• Access and Prompt Detection Rule Enforcement: Two rule types, Access rules which offer restrictions on attributes including user, application, LLM model, and version and Prompt detection rules (input/output checks for threats and policy violations), with response modes ranging from Report-only through Block to Replace (e.g., AWS tokens auto-redacted before prompt submission).

• AI Agent and MCP Server Runtime Security: Extends Falcon telemetry to AI agent workloads, MCP server interactions, and AI/API gateway traffic, providing runtime guardrails for engineering teams building and deploying agentic applications.

• Cross-Platform Telemetry Correlation: Feeds AIDR findings into Falcon Next-Gen SIEM for correlation with endpoint, cloud, and identity signals from across the full Falcon platform, enabling unified investigation across all telemetry sources.

• Browser-Layer Enforcement (via Seraphic): Extends enforcement into the browser execution context through the Seraphic acquisition, covering browser-mediated AI tool interactions and web application data flows.

Use Cases and Pain Points Addressed

1. Enterprise GenAI Tool Governance at Scale: Monitors and controls employee use of ChatGPT, Claude, Copilot, and other GenAI tools across the fleet, enforcing access policies and prompt-content rules at enterprise scale.

2. AI Application and Agentic Workload Runtime Security: Provides runtime guardrails for engineering teams building AI agents, chatbots, and agentic pipelines, detecting prompt injection, secret leakage, and policy violations within the development and production workflow.

3. Unified SOC Triage Across Endpoint and AI Telemetry: Surfaces AI-layer threat detections (prompt injections, data leakage, policy violations) in Falcon Next-Gen SIEM alongside endpoint and identity alerts, enabling SOC analysts to correlate AI-era incidents with traditional threat telemetry in a single pane.

Key Takeaway / Recommendation

For CISOs running CrowdStrike as their primary EDR and SIEM platform, Falcon AIDR is the lowest-friction path to baseline AI governance coverage; it deploys through the existing Falcon management plane and surfaces findings in the existing SOC workflow, requiring no net-new vendor relationships. The platform convergence advantage is real: CrowdStrike’s distribution, attach rates, and cross-telemetry correlation are structural advantages no ECP startup can match in the near term. However, buyers should treat AIDR as a strong baseline rather than a best-of-breed solution across all ECP zones.

Palo Alto Networks (Koi Security)

Vendor Profile

Palo Alto Networks entered the ECP Zone 1 market through its acquisition of Koi Security, a startup focused on AI and software supply chain visibility at the network and endpoint boundary. The Koi acquisition gives Palo Alto Networks a wedge into the non-binary software governance category, extending the Cortex and Prisma platforms beyond traditional process-level telemetry into the discovery and risk-ranking of browser extensions, IDE plugins, MCP servers, and agentic toolchains. Rather than building agentless posture management organically, Palo Alto Networks is using M&A to accelerate its position in the category, integrating Koi’s supply-chain gateway capabilities with existing MDM and EDR integrations already present in the Cortex XDR stack for a more direct integration path. Additionally, Koi will remain available standalone, integrating with existing MDM and EDR tools regardless of vendor. The strategic bet is that Palo Alto Networks’s installed base and enterprise go-to-market can rapidly distribute a point solution into a platform-level posture capability.

Applicable ECP Zones:

• Zone 1

• Zone 2

• Zone 3

• Zone 4

• Zone 5

Products/Services Overview

• Koi Security (Standalone Today; Planned Integration into Cortex): Network supply-chain gateway providing discovery, risk-ranking and preemptive control of non-binary endpoint software, blocking installation of unauthorized MCP servers, AI models, AI agent toolchains, code packages, OS packages, IDE plugins, and browser extensions.

• Cortex XDR: Palo Alto Networks’s extended detection and response platform, now extended with Koi-derived supply-chain telemetry

• Prisma Browser / Talon (acquired): Browser-centric enforcement and visibility layer, relevant to Zone 3 Agent Runtime Observability (AI-EDR) as well.

Core Functions

• Supply Chain Discovery: Continuously discovers and inventories non-binary software (extensions, plugins, packages, MCP tooling) installed across the enterprise endpoint fleet.

• Risk Ranking and Posture Assessment: Scores discovered software against threat intelligence, permission scope, and behavioral signals to surface high-severity configuration liabilities before exploitation.

• MDM/EDR Integration: Rides existing CrowdStrike, Defender, and MDM telemetry to deliver posture findings without requiring a net-new agent footprint on every endpoint.

• Policy Enforcement via Network Gateway: Enforces allow/deny decisions at the network supply-chain layer, preventing installation of unauthorized toolchain components before they reach the endpoint.

• Policy Enforcement via Endpoint Integration: Provides real-time control of AI agent behavior at runtime on the endpoint, governing approved software and what AI agents are permitted to do as they execute.

• Platform Convergence: Feeds Koi-derived signals into Cortex XDR and Prisma AIRS for cross-correlation with process, identity, and cloud telemetry.

Use Cases and Pain Points Addressed

1. Shadow AI Toolchain Governance: Discovers and risk-ranks unapproved non-binary software including AI tools, MCP servers, and IDE copilot plugins, browser extension, code packages, AI models, and OS packages deployed by shadow builders.

2. Software Supply Chain Compromise Prevention: Detects compromised or malicious packages and browser extensions entering the enterprise via developer and agentic workflows.

3. AI Agent Runtime Behavior Control: Monitors and restricts approved AI agents’ actions during execution, including permission scope, access to sensitive data and credentials, deleting cloud infrastructure, or pushing code changes.

4. Compliance and Configuration Hygiene: Ensures non-binary software meets organizational security policy (permissions, provenance, version currency) for audit and governance purposes.

Key Takeaway / Recommendation

For CISOs with an existing Palo Alto Networks Cortex or Prisma investment, Koi Security’s integration represents the lowest-friction path to Zone 1 coverage; it leverages the incumbent agent and platform rather than adding a net-new vendor. However, buyers should scrutinize enforcement depth vs. visibility breadth as the Koi acquisition is early-stage integration, and real-time enforcement capabilities (block vs. report) may lag behind purpose-built agentless players in the near term. Palo Alto Networks’s strategic advantage is distribution and platform convergence.

SentinelOne (Singularity)

Vendor Profile

SentinelOne occupies a position in the ECP market as a platform convergence vendor for ECP which it achieved via organic expansion and acquisition, extending its Singularity Platform foundation across all five ECP zones simultaneously. SentinelOne’s expansion not only includes prompt-layer governance centers but also includes its Purple AI analyst layer, the Singularity Data Lake, and its growing AI Security Posture Management (AI-SPM) capabilities, extending the Singularity agent from classic endpoint telemetry into AI-era governance of workloads, agents, and data flows. SentinelOne’s strategic differentiation against CrowdStrike in the ECP context is its broad AI detection capabilities and open data architecture. Singularity Data Lake ingests native and third-party telemetry into a unified SOC triage surface, positioning SentinelOne as an integration hub for ECP point solutions rather than requiring exclusive use of SentinelOne-native capabilities. Purple AI extends the platform by surfacing AI-generated investigation narratives and autonomous response recommendations across all ingested telemetry, including AI tool governance events. SentinelOne’s AI-SPM and Prompt Security capability addresses the emerging need to discover and govern AI workloads, models, and agent pipelines deployed in cloud and endpoint environments, a Zone 1/3 adjacency that gives SentinelOne an entry point into the non-binary software governance and agent runtime observability space without a dedicated Zone 1 point solution acquisition.

Applicable ECP Zones:

• All Zones

Products/Services Overview

• Singularity Platform: Core kernel-level endpoint agent providing autonomous prevention, detection, and response in addition to process, file, network, and identity telemetry across the enterprise fleet; foundation for ECP zone expansion

• Purple AI: Agentic AI analyst embedded across the Singularity platform; surfaces AI-generated investigation narratives, threat hunting queries, and autonomous response recommendations across all telemetry sources

• Singularity Data Lake: Open, cloud-hosted telemetry lake ingesting SentinelOne-native and third-party data sources (including ECP point solutions) for unified SOC correlation and investigation

• AI-SPM (AI Security Posture Management): Discovery and risk assessment of AI workloads, models, agent pipelines, and cloud-deployed AI infrastructure, posture governance for the AI development and deployment lifecycle

• Unified Alert Management (UAM): Integration hub surfacing third-party security findings alongside SentinelOne-native detections for unified analyst triage

Core Functions

• Platform-Wide AI Analyst (Purple AI): Generates natural language investigation narratives and hunting queries from endpoint, identity, cloud, and AI governance telemetry, reducing analyst time-to-triage across ECP-relevant events.

• AI Workload and Model Posture Discovery (AI-SPM): Discovers AI models, agent frameworks, and AI-enabled applications deployed across cloud and endpoint environments, assessing configuration risk and permission exposure, a Zone 1 and 3 adjacent capability.

• Open Telemetry Ingestion (Data Lake): Ingests third-party ECP telemetry (DLP findings, identity signals, network events, EDR data, cloud workloads) into Singularity Data Lake, enabling SentinelOne customers to use purpose-built ECP point solutions without losing SOC correlation.

• UAM Third-Party Alert Integration: Surfaces ECP point solution findings directly in the SentinelOne analyst triage queue, eliminating the operational silo between data movement governance and endpoint detection.

• Behavioral AI Detection (Singularity Agent): On-agent behavioral AI detecting anomalous process and application behavior at the endpoint, with expanding coverage into AI tool interaction patterns as the Singularity agent extends up-stack.

Use Cases and Pain Points Addressed

1. Unified SOC Triage Across ECP and EDR Telemetry: Ingests SentinelOne-native and ECP point solution findings into Singularity Data Lake and UAM, enabling SOC analysts to correlate AI governance events (data leakage, prompt injection, shadow AI activity) with endpoint and identity telemetry in a single investigation surface.

2. AI Workload and Agent Posture Governance (AI-SPM): Discovers and risk-ranks AI models, agent pipelines, and AI-enabled applications deployed in cloud and endpoint environments, providing CISOs with visibility into shadow AI infrastructure that traditional EDR telemetry does not surface.

3. Purple AI-Accelerated ECP Investigation: Applies Purple AI’s Agentic Investigation capability to ECP-sourced events, AI tool policy violations, and prompt injection detections, reducing mean time to investigate for complex multi-source AI-era incidents.

4. Automated Response via Hyperautomation: When Purple AI verdicts, AI-SPM findings, or UAM-ingested third-party alerts confirm a threat, Hyperautomation executes response automatically across the full stack. Automate endpoint containment, identity/session revocation, or any connected tool via pre-built SaaS connectors, a no-code custom connector, or Private Network Access for on-premises automation.

Key Takeaway / Recommendation

For CISOs running SentinelOne as their primary EDR and SIEM platform, the Singularity ecosystem provides a compelling integration hub for ECP point solutions: the open data lake architecture enables any Zone 1–5 vendor to feed SentinelOne without ripping out the incumbent. Purple AI’s agentic investigation capability is a meaningful analyst efficiency multiplier for ECP events, particularly for organizations that lack dedicated AI-era SOC expertise. Buyers should treat SentinelOne as the platform correlation and triage layer, augmented by purpose-built ECP vendors in Zones 1–4 for organizations with active agentic workloads. AI-SPM is the capability to watch for Zone 1/3 convergence as it matures.

Execution Strategy for the CISO and Security Team

SACR Five Strategic Zone Expansion Observations

1. SACR believes the zones converge into one architecture within ~3 years. Posture without enforcement is a report, enforcement without intent context is a false-positive machine and observability without prevention loses at machine speed. The winning stack is posture, inline gate, intent baseline and prevention enforcement in one user-mode agent.

2. Local AI on the endpoint becomes a defining moat. The cloud-pipeline cost structure of incumbent EDR becomes a liability at agentic telemetry volume. CrowdStrike’s network-dependent AIDR architecture is the specific vulnerability startups are targeting, as is Bold’s on-device classifier thesis. Agentics and small language models and generative AI-enabled behavioral defenses are emerging to help defend the endpoint (eventually leading to on-endpoint agentic defense).

3. Attribution becomes the new detection. The question shifts from “is this process malicious?” to “which prompt caused this action, and was it the user’s intent?”. Prompt and action lineage (Origin) and intent verification (like with Ent) are early versions of what becomes a required capability, the AI-era equivalent of the process tree.

4. The agentless wedge is real but time-boxed. Vendors ride-the-EDR model wins the next 18 months on deployment friction. As enforcement (not visibility) becomes the buying criterion, API depth and real-time enforcement capabilities limits will be a deciding factor. Visibility is commoditizing, while enforcement, telemetry depth, federation and speed of context are where we see the margin settle over time.

5. Platforms win the category, startups define it. The most likely outcome mirrors CSPM and Wiz: one or two startups (Zone 2/Zone 3 architectural natives best positioned) escape to platform scale while the rest are acquired. CrowdStrike AIDR’s attach rates prove the demand; the Koi acquisition shows platforms will buy rather than build the new zones.

Emerging Goal Pillars of Endpoint Control and Prevention

The modern Endpoint Control and Prevention (ECP) framework is defined by four critical operational pillars:

• App Posture & Discovery: Continuous governance of the non-binary software supply chain, including extensions and plugins.

• AI/Agent Observability & Attribution: Full traceability from natural language prompts to downstream tool execution.

• Intent, Identity, and Behavioral Inference: Shifting defense from binary monitoring to understanding the motivations behind user and agent actions.

• Real-time Policy Intervention: Surgical, inline enforcement that interrupts risky workflows mid-stream without disrupting legitimate business activity.

Investigation-Grade Evidence Required to Reconstruct Why, What and How

When an attack occurs, local telemetry is often the first and most detailed source of truth. However, raw endpoint logs (like standard Event IDs or process creation lines) lack the context required for sophisticated modern forensics. Endpoint agents must evolve into intelligent data collectors that correlate activities into a cohesive narrative. It isn’t enough to log that an administrative tool was run, a software or website was used; the endpoint must capture the prompt, tool, or automated workflow that triggered it. This allows SecOps to determine if a sequence of commands was executed by a human attacker, a benign developer, or an autonomous AI agent or toolchain.

Safer Enforcement: Moving Beyond Binary Decisions of Allow or Block through AI and Context Evaluation

Historically, endpoint detection and response (EDR) tools relied on binary enforcement where either a file was allowed to run, or the process was abruptly terminated and the machine isolated. This heavy-handed approach frequently breaks critical business workflows and alienates users. Modern endpoint defense requires graduated, human-centric controls integrated directly into the user interface. When a user attempts a risky but non-malicious action (e.g., running an unsigned IT tool or interacting with an unapproved API), the endpoint should inject real-time friction. This means warning the user, guiding them toward a safer alternative, or requiring a justified exception request that is immediately audited and passed to IT or GRC for review, rather than defaulting to a hard block. Contextual evaluation capabilities of LLMs have proven to reduce behavioral analysis of false positives by enabling cognitive evaluation by AI and agents, producing fewer false positives and more probabilistic-style responses.

Shifting from Posture Visibility to Proactive Posture Management and Control

The most disruptive change is the shift from endpoint security to posture security where the endpoint is no longer a boundary but a dynamic, self-optimizing component of the enterprise’s security ecosystem. The endpoint will be defined by identity, protected by identity, and continuously hardened by automated, intelligent systems. The technological building blocks are already in place: LLMs for reasoning, autonomous agents for self-healing, cloud-native and edge-native architectures for cross-platform security, and predictive posture for proactive defense. The next several years will see the convergence of these technologies into ECP, the next evolution of endpoint security. Endpoint defense must dynamically discover new micro-softwares and non-binary tools, rank their risk based on the permissions they hold (such as reading browser data, configurations, identities), evaluate source code repositories and supply chains for compromise, and apply proactive policy controls to prevent supply chain compromises arriving on endpoints.

SACR Future View: The Transition to Autonomous Endpoint Defense

By 2027, artificial intelligence and machine learning are fundamentally shifting Endpoint Detection and Response (EDR) from reactive, manual tools to proactive, autonomous, and self-healing systems conveying contextual information and telemetry in a semi-federated fashion.

The next horizon of endpoint defense is the migration of security intelligence from cloud-backend SIEM/SOAR clusters to the endpoint itself. As autonomous agents operate at machine speed, the traditional observe, export, analyze, respond cycle is becoming a structural failure point as the latency between detection and containment is now the primary attack vector.

The future of the endpoint is a self-governing runtime environment capable of endpoint and connected edge Inference. This requires a shift from passive telemetry collection to active, stateful full context defense, where endpoints utilize localized models (SLMs) and most likely include Edge services and integration with its telemetry to evaluate intent, govern non-binary software composition, and mediate agentic tool-chains in real-time, evolving from ECP to AECP over time.

ECP Zones as an Endpoint Control Plane

The architecture below presents The ECP Control Plane as a three-part architecture that moves from endpoint evidence to policy enforcement and verified outcomes. On the left, OS-layer signals, normalized endpoint context, and posture or behavioral state are consolidated into a validated endpoint evidence state. This evidence feeds a central, multi-zone enforcement model covering OS controls, software posture and supply chain governance, application-layer enforcement, agentic runtime visibility, intent and behavioral analysis, and data-centric enforcement. The model then applies zone-scoped write authority across endpoint agents, identity stores, browsers and SaaS applications, AI toolchains, and data egress layers, ultimately producing an enforced endpoint posture. A validation rail beneath the architecture logs each action, verifies the result against zone policy, preserves the evidence and authority record, and feeds the outcome back into the behavioral baseline.

ECP Zones as an ECP Control Plane

Key Requirements for Endpoint Control and Prevention

To enable autonomous Endpoint defense, security architectures must evolve to monitor these specific high-fidelity telemetry streams:

• Prompt-to-Tool Lineage: Real-time tracking of input prompts, the specific tools invoked by the agent, and the associated data movement.

• Non-Binary Composition State: Continuous monitoring of browser extensions, IDE plugins, and dependency packages (npm, PyPI) that alter the endpoint’s function post-deployment.

• Local Intent Inference: Evaluation of natural language intent against security policies using lightweight, on-device models to detect semantic anomalies before execution.

• Identity State Context: Localized tracking of active session tokens, OAuth grants, and API keys, ensuring that sensitive identity artifacts are only accessible to authorized workflows.

• Dynamic Policy Enforcement: Monitoring and identification of shadow builder activity with automated, no-code and low-code workflows assembled by non-technical teams, to assess configuration and permission risks at the moment of creation.

Market Map: Endpoint Control and Prevention

Future View: Endpoint Control and Prevention (ECP)

The integration of advanced AI and capabilities such as model context protocol driving the federation of agent-driven integrations are driving several major transformations in how EDR operates:

The Rise of Autonomous Agents

Endpoint security is entering its fifth generation, characterized by autonomous security agents. Instead of simply alerting human analysts, agentic AI is being deeply embedded into daily security operations, allowing systems to analyze massive volumes of heterogeneous data, make independent decisions about threat severity, and automatically trigger defensive reactions. This drastically reduces the mean time to respond (MTTR) from hours or days to just minutes, which is critical for containing fast-moving threats like ransomware.

New Attack Vectors Targeting AI

As AI becomes central to ECP, the AI models and agents themselves become high-value targets. Security teams must now defend against adversarial machine learning, where attackers manipulate input data to deceive AI detection algorithms. Because AI agents can crawl data and execute shell commands, they are vulnerable to indirect prompt injection attacks hidden within files or web pages. Modern ECP platforms are expanding to monitor AI usage, protect against data leakage, and secure the underlying AI infrastructure.

Advanced Behavioral Detection and LLM Integration

AI is replacing outdated signature-based detection with sophisticated behavioral analytics. EDR platforms are leveraging a combination of supervised learning, unsupervised learning, reinforcement learning (RL), and deep learning (DL) neural networks to recognize complex, novel attack patterns that traditional tools miss. Furthermore, Large Language Models (LLMs) are being deployed to parse massive amounts of unstructured data, such as chat logs, security logs, and emails to classify anomalies and conduct post-incident analysis by revealing an attacker’s motives and methods.

Self-Healing Endpoints and Automated Remediation

Perhaps the most significant advancement is the introduction of self-healing capabilities. When an endpoint is compromised, AI-driven ECP can automatically isolate the device, notify identity systems and backend telemetry via model context protocol (MCP), terminate malicious processes, remove persistence mechanisms, and roll back unauthorized system changes or encrypted files to a known-good, pre-infection state. This automated remediation and contextual sharing and response limits the blast radius of an attack and dramatically cuts down on the need to manually re-image devices.

Adaptive Security Policies

Rather than relying on rigid, static rules, AI empowers EDR and cloud security systems with continuous self-learning. These systems can dynamically adjust access controls, firewall settings, and anomaly thresholds in real time based on evolving attack patterns, ensuring resilience against zero-day exploits and environmental changes.

Human-Machine Collaboration

Despite the push for autonomy, a major theme for 2026 is balancing AI with human control so that algorithms do not go unchecked. The most effective SOCs utilize a collaborative model: autonomous systems handle routine threat containment and initial data correlation, while escalating the most sophisticated attacks to skilled human analysts. Human analysts then provide feedback that is used to continuously train and refine the machine learning models, improving future detection accuracy and reducing false positives.

Future View: Agentic Endpoint Control and Prevention (A-ECP)

Implications for CISOs / Buyers: 30/90 Day Plan

Buyer strategy should assume rapid convergence: incumbent EDR will add more app-layer posture and identity context, while posture/agent-control vendors will add response and evidence features. CISOs should leverage the SACR 5 Zones framework as the primary architectural tool for identifying coverage gaps and mapping their infrastructure needs. The practical evaluation lens is: evidence quality, intervention safety, and measurable exposure reduction.

30 Days: Assess and Baseline

1. Audit existing security coverage against the SACR 5 Zones framework to identify specific architectural coverage gaps and avoid redundant tooling.

2. Treat classic EDR as baseline plumbing, not the strategic differentiator. Reallocate budget and evaluation energy toward application-layer posture, agent observability, and intervention safety.

3. Run privacy and governance diligence early. Recognize that intent layers require new, sensitive telemetry (e.g., interaction traces). Validate your organization’s standards for minimization, purpose limitation, and auditability.

4. Track pricing/model changes as a strategic signal. Monitor endpoint per-seat pricing compression, which signals detection commoditization. Look for consumption/usage models that better align with modern, bursty agentic workflows.

90 Days: Validate and Operationalize

5. Demand prompt-to-action attribution in any agentic tooling environment. Ensure any selected vendor can connect intent/prompt → action trace; without this, investigations will fail at machine speed.

6. Prioritize enforcement primitives that are surgical. Shift away from blanket block/allow strategies. Evaluate flow-level, policy-based interventions and exceptional UX that won’t break critical business workflows.

7. Evaluate autonomy. Evaluate autonomy as an operations capability, emphasizing the integration of new ECP signals into existing SOC workflows rather than mere validation. Ensure that these signals are actionable in your current incident response processes.

8. Prepare for category convergence. As EAPM/posture vendors add interventions and intent vendors add posture, focus your final evaluation criteria on the quality of control primitives and evidence.

SACR Key Takeaway:

For CISOs, the transition to Endpoint Control and Prevention (ECP) is not merely a tool upgrade but a strategic necessity to reclaim visibility in an era of AI-driven, machine-speed threats. As the center of gravity shifts from process-based detection to interaction-centric governance, the key takeaway is that your endpoint strategy must prioritize prompt-to-action attribution and granular, surgical enforcement over legacy, binary blocking.

By pivoting to an ECP-ready/AI oriented architecture, focused on software supply chain posture, identity-centric context, and autonomous behavioral inference, you can effectively reduce the blast radius of agentic workflows while ensuring your security posture remains resilient against industrialized, high-speed exploitation.

References

• Deepwatch

• Kiteworks

• SACR Data & AI security

• Supply Chain Compromise

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Cybersecurity is entering a new phase where defensive advantage may depend less on detecting attackers and more on influencing them. As AI compresses attack timelines and organizations deploy growing numbers of autonomous systems, security architectures built around human investigation and response are coming under increasing pressure. A new generation of deception technologies is emerging to address this shift by moving beyond static decoys toward adaptive systems designed to shape attacker behavior in real time.

This report argues that agentic deception represents more than an incremental evolution of traditional deception technologies. It reflects a broader architectural shift from passive observation toward active environmental influence. Instead of waiting to detect malicious activity after it has begun, agentic deception seeks to manipulate the information, context, and decision-making processes that attackers, whether human or autonomous, rely upon throughout an intrusion. While the category remains in its early stages, it introduces an important question for security leaders: if offensive operations continue to accelerate, should defensive architectures evolve from observing attacks to actively shaping them?

The report examines this emerging category through the lens of Acalvio, one of several vendors exploring adaptive deception across identity, cloud, and AI-enabled environments. Acalvio’s platform serves as a case study for understanding how deception technologies are evolving, where they may offer strategic value, and which questions remain unresolved as the market matures. The premise underlying this category is that in a machine-speed environment, visibility alone arrives too late to change outcomes. If that premise holds, advantage shifts toward organizations that can actively shape what an attacker perceives, not merely observe what an attacker does.

Key Takeaways For Readers

• Agentic deception represents an architectural shift, extending deception from static assets toward adaptive systems that can influence attacker behavior during reconnaissance and early intrusion stages.

• AI changes both sides of the security equation. Organizations must defend not only against autonomous attackers but also protect rapidly expanding AI-enabled enterprise environments.

• Environmental influence is emerging as a complementary security control. Deception should be evaluated alongside detection, response, identity, and runtime protections rather than as a replacement for them.

• The market remains early. While platforms such as Acalvio demonstrate how adaptive deception may evolve, organizations should prioritize measurable operational outcomes, independent validation, and integration with existing security architectures over visionary claims alone.

What Security Leaders Should Do

Security leaders should begin evaluating deception as an architectural capability instead of a niche point product. As part of that evaluation, organizations should:

• Assess whether existing security controls can operate effectively against automated attacks.

• Determine where adaptive deception can complement existing detection, identity, and response capabilities.

• Evaluate how deception platforms integrate across cloud, identity, and AI-enabled environments.

• Request independent evidence of operational effectiveness, including customer deployments, MITRE ATT&CK or ATLAS mappings, and measurable production outcomes.

• Consider deception as one component of a broader strategy for reducing attacker certainty rather than simply increasing defensive visibility.

Why Deception Is Returning

For much of the past decade, deception occupied a relatively specialized place within enterprise security architectures. Honeypots, honeytokens, and decoy systems proved valuable for generating high-confidence alerts and exposing attacker behavior, but they were often deployed selectively, required ongoing maintenance, and rarely became foundational components of security programs. As organizations invested heavily in prevention, detection, and response technologies, deception remained an effective but niche capability.

That context is beginning to change.

Two parallel shifts are reshaping how defenders think about the role of deception. The first is the rapid acceleration of offensive operations through automation and AI. Recent industry reports indicate the time to detect an active breach remains substantial, approximately eight months (241 days) on average, per IBM’s 2025 report, underscoring how wide the gap remains between attacker speed and defender response time. The luxury of time, the days or weeks defenders once used for investigation, has diminished significantly. Machine-speed attacks have compressed the window of opportunity. The 2026 Verizon DBIR further corroborates this trend, as does the graph below. This graph from Zero Day Clock depicts the vast change of pace of vulnerability and exploit weaponization. This is one of the key drivers to moving to new defensive measures. Note: 2026 data reflects partial-year findings as of July 2026.

The significance of this trend is not in any specific data point. The significance is the trajectory: a time-to-exploit window that has collapsed from years to hours highlights a future in which portions of the attack lifecycle operate at machine speed. As attack timelines compress, the effectiveness of defensive models built around human investigation and decision-making may come under increasing pressure. This dynamic sits at the center of the broader discussion surrounding adaptive defense, autonomous response, and agentic deception.

The second shift is occurring inside the enterprise itself. Organizations are rapidly deploying AI assistants, autonomous workflows, retrieval-augmented generation (RAG) systems, and Model Context Protocol (MCP) environments to improve productivity and automate business processes. These systems create a fundamentally different attack surface. Instead of exploiting software vulnerabilities alone, adversaries may seek to manipulate prompts, poison context, abuse connected tools, or influence autonomous decision-making. As AI becomes embedded throughout enterprise operations, security teams must consider not only how to protect traditional infrastructure, but also how to protect systems that reason, retrieve information, and act on behalf of users.

Taken together, these trends are changing the assumptions that have guided cybersecurity for decades. Security architectures have historically focused on preventing compromise where possible, detecting malicious activity when prevention fails, and responding before attackers achieve their objectives. That sequence assumes defenders have sufficient time to observe events, investigate them, and coordinate a response. As attack timelines continue to compress, that assumption becomes difficult to sustain.

The New Security Reality

Defenders must now manage a future where three distinct elements coexist:

• Autonomous Attackers: AI systems that can execute exploits at scale.

• Autonomous Enterprise Systems: The AI tools that power our business processes.

• Automated Security Controls: The defense mechanisms attempting to keep pace.

The remainder of this note focuses on the interaction between the first two, and on whether the third category can evolve from passive detection into active environmental influence.

Why Visibility Isn’t Enough

While it remains unclear whether the future of defense lies in deception, autonomous response, or adaptive security, one truth is emerging: Cybersecurity is shifting. In this new world, speed, adaptability, and active decision-making are as critical as visibility.

The core question for future security architecture is simple: Can we build mechanisms that not only detect and respond to threats, but actively influence the environment, data, and decisions that adversaries rely on?

This does not mean detection and response are becoming obsolete. Visibility, investigation, and remediation remain essential components of every mature security program. However, organizations are exploring whether additional controls can operate earlier in the attack lifecycle, reducing attacker confidence and influencing outcomes before a compromise is fully established.

This renewed interest has brought deception technologies back into focus.

Unlike traditional security controls, deception is designed to influence what attackers perceive about an environment. Rather than simply identifying malicious activity after it occurs, deception seeks to shape reconnaissance, redirect adversary behavior, and introduce uncertainty into the information attackers rely upon to make decisions. In an era where both attackers and enterprise systems are becoming more autonomous, that ability to influence the environment may become an valuable complement to detection and response.

Whether adaptive deception ultimately becomes a foundational component of enterprise security remains an open question. The category is still maturing, and many of its architectural claims require broader operational validation. What is becoming clear, however, is that deception is no longer being evaluated solely as a collection of honeypots or decoy credentials. It is increasingly being considered as part of a broader discussion about how defensive architectures must evolve as automation transforms both sides of the cybersecurity equation.

From Honeypots to Agentic Deception

Deception is one of cybersecurity’s oldest defensive concepts. Long before today’s discussions of AI-native security, organizations deployed honeypots, decoy services, and honeytokens to expose attacker activity that would otherwise remain invisible. Their value stemmed from a simple principle: legitimate users should never interact with deceptive assets. When they did, defenders gained a high-confidence signal that malicious reconnaissance or unauthorized access was underway.

Despite these strengths, deception remained a specialized capability for much of the last two decades. Traditional deployments often required careful planning, manual placement, and ongoing maintenance to remain believable as production environments evolved. As cloud adoption accelerated and enterprise infrastructure became dynamic, maintaining realistic deception environments grew more operationally intensive. Many organizations concluded that deception was valuable for targeted use cases, but difficult to operate as a broad architectural control.

Today, several trends are prompting a reassessment of that conclusion.

Enterprise environments have become significantly more distributed across cloud platforms, SaaS applications, identity providers, and hybrid infrastructure. At the same time, attackers have become more reliant on automation to accelerate reconnaissance, privilege discovery, and lateral movement. Instead of manually exploring environments over days or weeks, adversaries can now leverage automation to identify relationships, enumerate identities, and discover attack paths at a pace that challenges traditional investigative workflows.

This convergence changes the requirements for deception itself. Static decoys that are updated periodically may no longer be sufficient in environments that change continuously. In light of this, organizations are asking whether deception can become adaptive, automatically reflecting changes in infrastructure, identities, cloud resources, and AI-enabled workflows without extensive manual intervention.

The evolution of deception can therefore be understood as a progression through several architectural stages.

This progression reflects more than incremental product development. It illustrates a broader shift in defensive philosophy. Earlier deception technologies focused primarily on revealing attacker activity. Emerging approaches seek to shape attacker decision-making by manipulating the information available during reconnaissance, credential discovery, and lateral movement.

Importantly, agentic deception should not be viewed as a replacement for existing security controls. Organizations still require prevention, detection, investigation, and response capabilities to manage modern cyber risk. Instead, deception is being evaluated as an additional architectural layer that operates earlier in the attack lifecycle, complementing identity security, endpoint detection and response (EDR), extended detection and response (XDR), and cloud security by influencing attacker behavior before objectives are achieved.

Not every vendor defines this evolution in the same way. Some continue to emphasize high-confidence detection through deception. Others focus on attack path disruption, moving target defense, or identity-centric deception. More recently, several vendors have begun extending deception into AI-enabled environments, including agent frameworks, retrieval systems, and Model Context Protocol (MCP) deployments. While implementation strategies differ, they share a common objective: reducing attacker certainty and increasing the cost of successful reconnaissance.

Viewed through this broader lens, agentic deception is best understood not as a single product category, but as one possible direction in the evolution of adaptive cyber defense. Whether it ultimately becomes a foundational component of enterprise security will depend on its ability to demonstrate measurable operational value at scale.

The market for deception technologies is still undergoing a period of architectural convergence. While vendors use terms such as adaptive, dynamic, or agentic deception, there is no universally accepted definition of these concepts, and implementation maturity varies considerably across the market. Organizations should therefore evaluate vendor claims based on demonstrable operational outcomes rather than terminology alone. The defining characteristic of this emerging category is not the presence of AI, but the extent to which deception can adapt to changing environments, integrate with existing security operations, and measurably influence attacker behavior without introducing prohibitive operational overhead.

Defining the Agentic Deception Category

Cybersecurity categories often evolve faster than the language used to describe them. Terms such as AI-native security, adaptive defense, runtime protection, and agentic security are appearing in vendor messaging, yet they frequently describe overlapping capabilities and not clearly defined markets. Deception technologies are experiencing a similar transition. While many vendors now position their platforms as adaptive or autonomous, there is not yet a widely accepted definition of what distinguishes agentic deception from earlier generations of deception technology.

SACR defines agentic deception as:

An adaptive security architecture that continuously modifies deceptive assets, environmental signals, and engagement pathways to influence attacker behavior throughout the attack lifecycle.

Unlike traditional deception technologies, which primarily function as detection mechanisms, agentic deception seeks to shape the attacker’s understanding of the environment itself. Its objective is not simply to identify malicious activity after reconnaissance has begun, but to reduce the accuracy of the information an adversary uses to make decisions. In this model, deception becomes an active participant in the engagement rather than a passive tripwire waiting to be triggered.

This distinction is particularly important as enterprise environments become more dynamic. Cloud infrastructure changes continuously. Identity relationships evolve. AI agents interact with external tools and data sources. In these environments, static deception assets can lose relevance if they fail to reflect operational reality. Agentic deception attempts to address this challenge by continuously adapting deceptive resources and engagement strategies as the environment and attacker behavior change.

At a conceptual level, agentic deception combines several established security disciplines:

• Deception technology, to create believable but controlled assets that reveal malicious activity.

• Moving target defense, to reduce attacker confidence by limiting the reliability of environmental observations.

• Threat intelligence, to increase the realism and relevance of deceptive environments.

• Automation and AI, to continuously adapt deception without requiring extensive manual administration.

None of these capabilities is entirely new on its own. The architectural shift lies in bringing them together as a coordinated system designed to influence adversary decision-making throughout an engagement rather than performing isolated security functions.

What Agentic Deception Is Not

As the category develops, it is equally important to define what agentic deception does not encompass.

• It is not a replacement for endpoint detection and response (EDR), extended detection and response (XDR), or security information and event management (SIEM). Those platforms remain essential for telemetry collection, investigation, correlation, and incident response.

• It is not security orchestration, automation, and response (SOAR), which focuses on automating defensive workflows after alerts have been generated.

• It is not breach and attack simulation (BAS), whose primary objective is to assess defensive readiness through controlled testing and not just influence live adversary behavior.

Nor should it be viewed as a substitute for identity security, cloud security, or AI runtime protections. Instead, agentic deception is best understood as an architectural layer that complements these capabilities by introducing uncertainty into the reconnaissance and decision-making processes that precede many successful attacks.

Where Agentic Deception Fits

Rather than replacing existing security controls, agentic deception introduces an adaptive layer designed to influence attacker behavior before traditional detection and response processes become necessary.

Why This Matters for Security Leaders

The emergence of agentic deception reflects a broader shift in how security leaders are thinking about defensive architecture. Historically, organizations have measured the effectiveness of security controls by their ability to prevent compromise, detect malicious activity, or accelerate response. Historically, cybersecurity has optimized for visibility. However, visibility alone arrives too late to change outcomes. Adaptive deception introduces a second optimization objective: attacker uncertainty. Security architectures that maximize visibility without affecting attacker confidence will struggle against autonomous adversaries capable of making decisions at machine speed.

Whether this architectural approach ultimately becomes a standard component of enterprise security remains uncertain. The market is still early, terminology continues to evolve, and independent operational validation remains limited. Nevertheless, the underlying concept, influencing attacker behavior and not just simply observing it, represents a meaningful evolution in defensive thinking and provides a useful framework for evaluating the next generation of deception platforms.

SACR Analysis

The defining characteristic of agentic deception is not the use of AI. Many security products now incorporate AI-assisted analytics or automation without fundamentally changing how they influence attacker behavior. What distinguishes this emerging category is the combination of continuous adaptation, environmental awareness, and behavioral influence into a unified defensive architecture.

Organizations evaluating vendor claims should therefore focus less on whether a platform is marketed as agentic and more on whether it demonstrably adapts to changing environments, integrates with existing security operations, and measurably alters attacker outcomes. As with many emerging cybersecurity categories, marketing terminology has evolved faster than common industry definitions. Buyers should evaluate architectural capabilities and operational evidence and not just product labels.

Architectural Principles of Adaptive Deception

As deception technologies evolve beyond static decoys, they are being designed around a different objective. Instead of functioning solely as isolated detection mechanisms, emerging platforms seek to influence how attackers perceive, interpret, and navigate enterprise environments. While implementations vary across vendors, several architectural principles are beginning to define this new generation of adaptive deception.

These principles represent a shift in defensive philosophy rather than a single product architecture. Organizations evaluating deception platforms should consider how effectively vendors operationalize each capability, regardless of whether they use identical terminology.

Environmental Influence, Not Passive Observation

Traditional security architectures are designed to observe malicious activity as accurately and as early as possible. Firewalls block unauthorized access, endpoint agents generate telemetry, and SIEM platforms aggregate evidence for investigation. Each control contributes to understanding what has occurred.

Adaptive deception introduces a complementary objective: influencing what attackers believe about the environment before they achieve their objectives.

Instead of treating reconnaissance as a precursor to compromise, adaptive deception treats it as an opportunity to shape attacker perception. By presenting carefully controlled environmental signals, deceptive credentials, services, identities, or infrastructure, defenders can reduce the reliability of the information attackers depend upon to make decisions.

This does not eliminate the need for detection or response. Instead, it seeks to improve defensive outcomes by reducing attacker certainty earlier in the engagement.

Reconnaissance Becomes a Defensive Opportunity

Reconnaissance has historically favored the attacker. Service banners, Active Directory relationships, cloud metadata, API responses, storage locations, and identity relationships all contribute to an ever more accurate understanding of the target environment. Every successful observation improves the attacker’s ability to prioritize exploitation and lateral movement.

Adaptive deception attempts to invert that advantage, instead of allowing reconnaissance to function solely as an intelligence-gathering activity, deception introduces controlled uncertainty into the process. If attackers cannot reliably distinguish production assets from deceptive ones, the quality of their intelligence begins to degrade. The objective is not to prevent reconnaissance entirely, but to reduce its usefulness.

Every interaction becomes an opportunity to gather intelligence, influence attacker decision-making, and improve future defensive responses.

Adaptation Becomes More Important Than Static Coverage

One of the historical limitations of deception has been operational maintenance. Static deception environments often require administrators to manually deploy and update decoys as infrastructure changes. In modern enterprises, where cloud resources, identities, workloads, and AI services evolve continuously, manually maintaining believable deception assets becomes yet more difficult.

Adaptive deception seeks to address this challenge by automating portions of the deception lifecycle. Rather than relying on periodically refreshed assets, platforms attempt to adjust deception in response to environmental changes, infrastructure updates, or observed attacker behavior. The degree of automation varies significantly across vendors, but the underlying objective remains consistent: deception should evolve alongside the environment it is intended to protect.

This capability is particularly important in cloud-native environments, where infrastructure may be created and retired in minutes instead of months.

Identity and AI Expand the Scope of Deception

Traditional deception focused primarily on servers, endpoints, and network infrastructure. Modern attack paths center on identities, cloud services, APIs, privileged access, and AI-enabled workflows. As enterprises adopt AI assistants, retrieval-augmented generation (RAG) systems, and Model Context Protocol (MCP) ecosystems, the environments attackers seek to manipulate extend well beyond conventional infrastructure.

Consequently, deception is expanding into these operational domains. Emerging platforms are beginning to deploy deceptive identities, cloud artifacts, configuration data, AI-specific resources, and contextual information designed to reveal or influence malicious activity targeting modern enterprise environments.

Whether these approaches prove effective at scale remains an open question. However, they reflect an important shift: deception now follows the attacker instead of remaining confined to traditional infrastructure.

Intelligence Becomes a Two-Way Process

Many security controls rely primarily on external intelligence.

Threat intelligence feeds, vulnerability databases, and indicators of compromise help organizations understand threats that have already been observed elsewhere.

Deception introduces an additional source of intelligence generated inside the organization itself.

Interactions with deceptive assets can reveal attacker objectives, reconnaissance techniques, privilege assumptions, and lateral movement strategies that are specific to the protected environment. Because legitimate users should rarely interact with deception assets, these engagements often produce higher-confidence signals than traditional telemetry alone.

This intelligence can improve investigations, refine detection logic, support incident response, and provide greater visibility into how adversaries operate within the organization’s own environment.

Analyst Perspective: The Shift Is Architectural

The significance of adaptive deception lies less in any individual capability than in how these capabilities work together.

Traditional deception sought to answer a relatively narrow question of whether an attacker has interacted with something they should never have touched.

Adaptive deception asks a broader one, of whether the environment itself can become an active participant in the defensive process.

That distinction reflects an architectural shift and not simply a product enhancement. The market is moving from isolated deception assets toward systems that continuously adapt, generate intelligence, and influence attacker behavior across increasingly dynamic enterprise environments.

Whether this transition ultimately reshapes enterprise security remains uncertain. Independent validation is still limited, terminology has yet to converge, and vendors vary significantly in implementation maturity. Even so, the broader direction reflects a growing recognition that defensive advantage may depend not only on observing attacks, but on influencing the conditions under which they unfold.

Case Study: Acalvio’s Approach to Agentic Deception

While the principles outlined in the previous sections describe the broader direction of adaptive deception, vendors are implementing those concepts in different ways. Some emphasize identity-centric deception, others focus on cloud infrastructure or attack-path disruption, while others extend deception into AI-enabled environments. The market remains early, and there is no single architectural model that has yet emerged as the industry standard.

Among the vendors shaping this evolution, Acalvio has articulated one of the more comprehensive visions for adaptive deception. Instead of positioning deception as an isolated detection capability, the company frames it as an operational layer that continuously adapts to attacker behavior, generating intelligence while attempting to influence adversary decision-making throughout an engagement.

SACR views Acalvio not as representative of the entire category, but as a useful case study for understanding how agentic deception may develop over the coming years. The following assessment is based on product demonstrations, technical briefings, publicly available documentation, and analyst evaluation. Architectural capabilities discussed below should be interpreted as vendor-stated implementations unless otherwise noted.

Architectural Overview

• Threat Exposure and Attack Path Analysis: Acalvio automates threat exposure and attack path analysis, evaluating assets, privilege relationships, and likely attack routes to determine where deception can have the greatest strategic impact. Rather than placing decoys manually, the platform positions deception assets along the paths an adversary is most likely to take. This analysis forms the foundation for the platform’s downstream deception and engagement activities.

• Autonomous Deception Creation (Generative Deceptions): To address the operational challenge of scale, Acalvio automates the creation of deception assets instead of requiring manual builds for every decoy, generating systems, credentials, cloud resources, and identity artifacts designed to align with the protected environment. SACR refers to this as generative deception creation: a capability that targets a real bottleneck in traditional deployments, though it has not yet been demonstrated at production scale.

• Identity-Centric Deception: Because identity has become one of the most valuable control planes for attackers, Acalvio extends deception into identity-centric attack paths through deceptive credentials, privileged account artifacts, and access relationships designed to appear operationally relevant while remaining isolated from production resources. This gives the platform a mechanism for detecting and influencing credential-based lateral movement specifically, not relying on network-level deception alone.

• Cloud-Native Deception: Acalvio extends deception into cloud control planes by generating deceptive cloud resources, credentials, and storage artifacts that mirror the API-driven, identity-dependent nature of cloud infrastructure. This follows attackers into management planes and storage services where much of today’s critical infrastructure resides, not just confining deception to endpoints and network infrastructure.

• Targeted Threat Intelligence: Acalvio’s targeted threat intelligence capability synchronizes deception content with the tactics, techniques, and behaviors most likely to target a specific enterprise, rather than relying on generic decoys. This contextual relevance is intended to make deception assets more believable and therefore more effective at attracting engagement, forming a feedback loop between threat intelligence, environmental visibility, and decoy deployment.

• AI Infrastructure Protection: Acalvio extends deception into AI-enabled environments, agent frameworks, retrieval systems, and MCP deployments, creating visibility into attack surfaces that fall outside traditional infrastructure. The specific mechanisms here (MCP decoys, deceptive tools, configuration breadcrumbs) are detailed under “MCP Decoys and AI Infrastructure Deception” later in this case study, where they connect to the MITRE ATLAS-mapped operational demo.

Acalvio’s ShadowPlex platform is designed around a continuous feedback loop in which deception assets evolve alongside both the protected environment and observed attacker behavior. Instead of deploying static decoys that require periodic maintenance, the platform attempts to automate the creation, placement, and adaptation of deceptive resources across identity systems, cloud infrastructure, enterprise networks, and emerging AI-enabled environments.

Operational Value

Individually, these six modules address distinct operational gaps: attack path analysis improves decoy placement, automated generation addresses scale, identity and cloud deception extend coverage into modern control planes, targeted threat intelligence improves realism, and AI infrastructure protection extends deception into emerging environments. Collectively, they form the foundation for the continuously adapting system described below.

The Acalvio Workflow: A Continuous Loop of Influence

At a conceptual level, the workflow can be summarized as a continuous cycle:

Detect → Redirect → Deceive → Learn → Adapt

The platform seeks to connect these functions into an ongoing operational process. Information gathered during reconnaissance, credential access attempts, or lateral movement is intended to inform subsequent adjustments to the deception environment, creating a feedback loop that evolves as new activity is observed.

ShadowPlex as an Implementation of Adaptive Environmental Control

One of the distinguishing characteristics of Acalvio’s ShadowPlex architecture is that it treats deception not as a collection of individual decoys, but as a continuously operating defensive layer that evolves alongside the enterprise environment. This reflects a broader architectural shift discussed throughout this report: deception is moving from a static detection capability toward an adaptive environmental control designed to influence attacker behavior before traditional response processes begin.

Historically, most deception platforms have focused on placing believable but isolated artifacts throughout enterprise environments. Decoy systems, deceptive credentials, honeytokens, and fake services generated valuable telemetry when accessed by an adversary, but they generally remained passive until triggered. Their primary purpose was to reveal malicious activity after reconnaissance had already reached a deceptive asset.

ShadowPlex approaches the problem differently. Rather than treating deception as a discrete collection of traps, the platform attempts to model the surrounding environment and continuously adapt deceptive assets as identities, infrastructure, cloud resources, and attack paths evolve. In this model, deception becomes part of the operating environment itself and not just an isolated security capability deployed alongside it.

From an architectural perspective, this distinction is significant. Modern enterprise environments are increasingly dynamic. Cloud infrastructure scales automatically, identities gain and lose privileges, workloads migrate across platforms, and AI-enabled systems establish new relationships with enterprise data and external services. Static deception assets become progressively less believable if they fail to evolve alongside these changes. An adaptive control layer attempts to preserve realism by continuously aligning deceptive resources with the operational characteristics of the environment they are intended to emulate.

This shift also changes the defensive objective. Traditional deception platforms primarily sought to answer a binary question: Has an attacker interacted with an asset that should never have been accessed? Adaptive environmental control introduces a broader objective: Can the environment itself shape how an attacker understands, prioritizes, and navigates the enterprise before critical objectives are achieved?

In practical terms, this means deception is no longer evaluated solely by the alerts it produces. Its value depends on whether it can influence reconnaissance quality, alter attacker decision-making, generate higher-confidence operational intelligence, and create additional opportunities for defenders to intervene earlier in the intrusion lifecycle.

ShadowPlex illustrates one implementation of this architectural philosophy through a continuous operational cycle in which environmental observation informs deception generation, attacker interaction produces intelligence, and that intelligence contributes to subsequent adaptation. Rather than treating these activities as independent workflows, the platform seeks to integrate them into an ongoing feedback loop that evolves as both the protected environment and attacker behavior change.

This architecture aligns with a prevalent trend visible across multiple areas of cybersecurity. Identity security platforms now adjust trust decisions based on context. Cloud security continuously evaluates infrastructure posture as environments change. AI runtime security adapts policies according to agent behavior and data access patterns. Within this wider movement toward adaptive security, deception is evolving in a similar direction, from static artifacts toward continuously managed environmental controls.

Whether this architectural model ultimately becomes the dominant approach to deception remains an open question. Organizations should continue evaluating claims of autonomous adaptation through independent validation, production deployments, and measurable operational outcomes. Nevertheless, ShadowPlex demonstrates how deception can be reimagined as an adaptive security capability and not just an alert-generation mechanism. In doing so, it provides a useful illustration of the broader transition from passive observation toward active environmental influence that is reshaping this emerging category. The environment is not treated as static infrastructure but as a continuously evolving defensive surface.

How Acalvio Applies the Principles of Adaptive Deception

The platform demonstrates several of the architectural principles discussed earlier in this report.

Degrading Intelligence: Reconnaissance Redirection and Steering

Traditional environments often reveal valuable information through negative responses. Closed ports, rejected connections, unavailable services, and error messages all help attackers build accurate maps of a target environment.

Acalvio’s approach converts unused infrastructure from a source of negative responses into an instrument of influence. By redirecting probes into controlled environments, the platform is designed to poison the attacker’s reconnaissance; if effective, the adversary’s model of the target is inaccurate from the first packet. Acalvio claims this degrades the quality of attacker intelligence in real time.

The strategic objective is not merely detection. It is the degradation of reconnaissance quality.

Every probe becomes an opportunity to influence what an attacker believes to be true about the environment. Rather than collecting intelligence passively, the environment begins participating in the engagement itself.

Undermining Trust: Response Morphing

Attackers rely heavily on consistency when building an understanding of a target environment. Service banners, operating system fingerprints, protocol responses, and configuration details all contribute to an attacker’s model of the environment.

Response morphing seeks to undermine that process.

During the briefing, Acalvio demonstrated how environmental signals could be dynamically altered to create uncertainty. For example, infrastructure that appears to belong to one platform may respond in a manner consistent with another. The information remains believable, but its accuracy becomes less reliable.

Consistency is an attacker’s greatest asset. The company argues that response morphing aims to disrupt that consistency. By dynamically altering service banners, fingerprints, and protocol responses, the platform aims to make environmental signals untrustworthy; replacing accuracy with believable friction intended to push the adversary and their autonomous agents toward indecision. Whether sophisticated adversaries adapt to this technique over repeated exposure is an open question, one SACR flags in the fingerprinting risk below.

Dynamic Defense: Machine-Speed Adaptation

The most significant aspect of the ShadowPlex architecture is the continuous reprogramming of deception based on observed activity. As Acalvio observes credential attempts or lateral movement, it adjusts deception assets in real-time to match inferred goals. This intent-driven adaptation is designed to ensure that attackers encounter an environment changing faster than it can be modeled, positioning deception as a primary security control that meets compressed attack timelines with equivalent speed.

Every action reveals information. Reconnaissance activity identifies areas of interest. Credential access attempts suggest objectives. Lateral movement activity reveals assumptions regarding network structure and available resources. Rather than treating these activities as isolated events, the platform incorporates them into an evolving threat model.

The underlying design principle is that the environment must be tailored to the attacker, and re-tailored continuously as the attacker’s inferred goals evolve.

Environmental Guardrails

Much of the current discussion surrounding AI security focuses on controls that operate inside the model itself, such as alignment training, safety tuning, prompt filtering, and policy enforcement.

Acalvio’s approach introduces a complementary concept: environmental guardrails.

Instead of relying exclusively on model-level controls, environmental guardrails focus on influencing behavior through the surrounding environment. Context, access paths, resources, tools, and operational workflows can all be monitored, constrained, or manipulated to reduce risk.

This approach reflects a broader security principle. Organizations have historically relied on multiple layers of defense, not merely trusting any single control. Environmental guardrails extend that philosophy into AI-enabled environments.

As autonomous systems become more deeply integrated into enterprise operations, runtime controls may become important alongside model-level protections.

MCP Decoys and AI Infrastructure Deception

One of the more forward-looking aspects of the architecture is its application to emerging AI ecosystems.

As organizations deploy AI agents, tool frameworks, retrieval systems, and Model Context Protocol (MCP) environments, entirely new attack surfaces are being created. These systems introduce opportunities for attackers seeking to manipulate workflows, poison context, abuse tools, or influence agent behavior.

Acalvio extends deception directly into these environments through concepts such as MCP decoys, deceptive tools, configuration breadcrumbs, and AI-focused deception assets.

The objective is to identify malicious activity earlier in the attack lifecycle while generating intelligence regarding how adversaries interact with AI-enabled systems.

This represents an important expansion of traditional deception. Historically, deception focused on networks, endpoints, and applications. Agentic deception extends those concepts into the infrastructure supporting autonomous systems themselves.

Viewed collectively, these mechanisms illustrate a broader shift in defensive philosophy. Rather than focusing exclusively on observing attackers, the architecture attempts to influence what attackers see, what they believe, and ultimately how they behave.

Where Acalvio Appears Differentiated

Based on SACR’s evaluation, several aspects of the platform stand out relative to the broader direction of the deception market.

Integration across multiple control planes. Instead of treating deception as a network-centric capability, the platform extends into identity, cloud, and AI-enabled environments.

Operational automation. The emphasis on automating deception generation and lifecycle management addresses one of the historical barriers to enterprise adoption.

Forward-looking AI strategy. Acalvio has invested early in deception techniques targeting AI-native environments, particularly MCP-based workflows and agent ecosystems. Although this area remains immature across the industry, it reflects a recognition that enterprise attack surfaces are expanding beyond conventional infrastructure.

These differentiators should be viewed within the context of a rapidly evolving market. Competing vendors are pursuing similar objectives through different architectural approaches, and the long-term competitive landscape has yet to stabilize.

Areas Buyers Should Validate

As with any emerging cybersecurity category, architectural vision should be accompanied by operational validation.

Organizations evaluating Acalvio should seek evidence in several areas:

• How effectively does continuous adaptation perform in large, dynamic enterprise environments?

• Can deception assets resist fingerprinting during repeated engagements?

• What measurable impact does adaptive deception have on attacker decision-making and incident outcomes?

• How much operational overhead is required to maintain believable deception at scale?

• How seamlessly does platform-generated intelligence integrate with existing SIEM, XDR, identity, and cloud security workflows?

• What independent customer evidence supports the platform’s claims?

These questions are not unique to Acalvio. They represent broader evaluation criteria for the agentic deception category as a whole and should form part of any procurement process.

SACR Assessment

Overall, SACR views Acalvio as one of the more innovative vendors shaping the future of deception. While the broader category is still emerging, the company’s emphasis on adaptive defense, environmental influence, and AI-aware deception reflects where cybersecurity architectures are likely to evolve as organizations confront autonomous attackers and AI-enabled enterprise environments. For organizations exploring next-generation defensive strategies, Acalvio offers a differentiated vision that extends beyond traditional detection toward actively influencing attacker behavior.

Whether agentic deception ultimately becomes a foundational security category remains uncertain. However, the underlying direction is compelling. As organizations continue adopting automation and autonomous systems, defensive architectures may benefit from controls that do more than observe activity; they may also need to shape the conditions under which that activity occurs. Within that context, Acalvio offers a credible and differentiated vision of what adaptive cyber defense could look like in the years ahead.

Procurement Considerations for Security Leaders

The emergence of adaptive deception does not fundamentally change how organizations should evaluate security technologies. Rather, it expands the set of architectural questions security leaders should be asking as enterprise environments become dynamic and AI-enabled.

For many organizations, deception is unlikely to replace existing investments in identity security, endpoint protection, detection and response, or cloud security. Its value lies in complementing those controls by introducing additional opportunities to influence attacker behavior before critical objectives are achieved.

Security leaders evaluating deception platforms should therefore focus less on individual product features and more on how effectively those capabilities integrate into broader security operations.

Evaluate the Architecture, Not the Marketing

As the category matures, terminology is evolving rapidly. Vendors describe their platforms using terms such as adaptive, autonomous, AI-native, or agentic. While these labels may reflect genuine architectural innovation, they are not substitutes for demonstrable operational capabilities.

Instead, organizations should evaluate whether platforms can:

• adapt to changing enterprise environments with minimal manual effort;

• integrate across identity, cloud, endpoint, and AI-enabled ecosystems;

• generate actionable intelligence that improves broader security operations; and

• demonstrate measurable defensive outcomes beyond proof-of-concept environments.

Architectural maturity should ultimately be measured by operational effectiveness and not just product messaging.

Prioritize Operational Integration

Deception should strengthen existing security programs, integrating with them as a well-rounded capability.

Organizations should understand how deception-generated intelligence integrates with existing investments in SIEM, XDR, SOAR, identity security, cloud security, and incident response workflows. High-confidence detections are valuable only if they improve investigation, accelerate decision-making, and reduce operational complexity.

Similarly, adaptive deception should be evaluated within the broader security operating model. The technology is unlikely to deliver meaningful value if it introduces additional management overhead or requires extensive manual administration to remain effective.

Validate Adaptation, Not Just Detection

Many deception technologies have historically demonstrated their value by generating accurate alerts with relatively low false-positive rates.

Adaptive deception introduces a different claim: that the environment itself evolves as attackers interact with it.

This capability represents one of the most significant architectural shifts discussed throughout this report, but it is also one of the areas requiring the greatest scrutiny.

Organizations should therefore request evidence demonstrating:

• how deception adapts over time;

• how those adaptations improve defensive outcomes;

• whether adaptive behaviors remain effective against sophisticated adversaries; and

• how platform performance changes across large, dynamic enterprise environments.

The distinction between static automation and meaningful adaptation is likely to become an important differentiator as the market matures.

Consider AI as Part of a Broader Security Strategy

The rapid adoption of AI assistants, retrieval systems, autonomous workflows, and agent frameworks is expanding enterprise attack surfaces in ways that traditional security architectures were not designed to address.

While deception may provide useful visibility into these environments, it should be evaluated as one component of a broader AI security strategy that also includes governance, identity, runtime controls, application security, and data protection.

Organizations should avoid viewing any single technology as sufficient for securing AI-enabled environments. Instead, the objective should be to combine complementary controls that reduce both the likelihood and impact of autonomous attacks.

Questions Every Buyer Should Ask

Before selecting an adaptive deception platform, CISOs should be able to answer several practical questions.

• What operational problem is this platform solving that existing controls do not?

• How does it integrate with the organization’s current security architecture?

• Can it demonstrate measurable improvements in detection quality, attacker disruption, or investigation efficiency?

• How much operational effort is required to maintain realistic deception environments?

• What independent customer evidence supports the vendor’s architectural claims?

• How effectively does the platform extend across cloud, identity, and AI-enabled environments?

• What metrics will be used to determine whether the deployment has been successful?

These questions are ultimately more important than any individual capability. As with many emerging cybersecurity categories, long-term value will depend on operational outcomes, not architectural ambition alone.

Final Assessment

The renewed interest in deception reflects a broader transformation occurring across enterprise cybersecurity. As organizations deploy autonomous systems and attackers continue to automate reconnaissance, exploitation, and decision-making, security architectures built primarily around human-speed detection and response are being reevaluated.

Agentic deception represents one response to that shift.

Rather than focusing exclusively on identifying malicious activity after it has begun, adaptive deception explores whether defenders can influence the information, context, and environmental conditions that attackers rely upon throughout an engagement. Whether this approach ultimately becomes a foundational component of enterprise security remains uncertain. The category is still evolving, independent validation remains limited, and implementation maturity varies considerably across vendors.

Nevertheless, the underlying architectural direction is significant.

The future of cyber defense is unlikely to be defined by a single technology category. Instead, organizations will move towards combining identity security, runtime protection, cloud security, AI governance, detection and response, and adaptive controls into integrated security architectures capable of operating continuously across highly dynamic environments.

Within that broader evolution, deception appears to be moving beyond its historical role as a niche detection capability toward a more strategic function focused on environmental influence and operational intelligence.

Among the vendors contributing to this transition, Acalvio presents one of the more comprehensive implementations currently available. Its emphasis on continuous adaptation, identity- and cloud-centric deception, and AI-enabled environments aligns closely with several of the architectural trends shaping the market. While many of its longer-term claims remain to be validated through broader enterprise adoption, the platform provides a credible illustration of how adaptive deception may evolve over the coming years.

The strategic importance of deception in that environment is that it targets one of the most critical dependencies shared by both humans and machines: information. Ultimately, the significance of agentic deception lies less in any individual implementation than in the question it asks of modern security architecture:

If attackers rely on autonomous systems to understand and navigate enterprise environments, should defenders continue focusing solely on observing those systems, or should they also seek to influence what those systems perceive?

The answer will vary across organizations and will continue to evolve alongside the technology itself. What appears likely, however, is that the next generation of enterprise security will place greater emphasis not only on detecting attacks, but on shaping the conditions under which they unfold.

Disclosure: This report is commissioned and sponsored by Acalvio. SACR retains full editorial control, independence, and objectivity. Acalvio’s role is limited to providing briefing access, customer contacts, and factual review. Sponsorship does not influence SACR’s findings, competitive analysis, or recommendations.

Sources & Further Reading

Vendor Briefings and Product Materials

• Acalvio product demonstrations (2026)

• Acalvio technical briefings

• Acalvio ShadowPlex architecture documentation

• Acalvio MCP operational demonstration

• Acalvio solution documentation

Research & Standards

• IBM. Cost of a Data Breach Report 2025.

• Verizon Business. 2026 Data Breach Investigations Report (DBIR).

• MITRE Corporation. MITRE ATT&CK Framework.

• MITRE Corporation. MITRE Engage.

• National Institute of Standards and Technology. AI Risk Management Framework (NIST AI RMF 1.0).

• OWASP Foundation. OWASP Top 10 for Large Language Model (LLM) Applications.

• Microsoft. Microsoft Digital Defence Report.

• From Vulnerability to Exploitation – time-to-exploit dataset.

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Today, organizations are forced to adopt AI faster than their governance, procurement, and security assurance models can adapt. Existing AI security standards provide strong foundations, but organizations still face a translation problem. Many AI governance frameworks describe sound principles but often stop short of the technical enforcement and SecOps integration that operational teams need.

SACR developed the AI and Agentic Risk Management and Control Framework, or ARMCF, to help close that gap.

ARMCF is a practitioner-oriented operating framework for governing, securing, monitoring, responding to and recovering AI and agentic systems. It applies the familiar six-function lifecycle of NIST CSF 2.0: GOVERN, IDENTIFY, PROTECT, DETECT, RESPOND and RECOVER to the specific risks created by AI systems with access to data, identities, tools and downstream actions.

The framework helps organizations:

1. Maintain visibility into sanctioned and shadow AI systems.

2. Define risk appetite, accountability and acceptable levels of autonomy.

3. Assess data flows, dependencies and potential blast radius.

4. Preserve evidence, restore systems safely and improve controls after incidents.

5. Map control decisions to established security, AI governance and assurance frameworks.

The AI and Agentic Risk Management and Control Framework (ARMCF) was designed to help organizations govern, secure, monitor, and recover AI and agentic systems using a lifecycle model that aligns with familiar cybersecurity and risk disciplines. It synthesizes established concepts from NIST AI RMF, NIST SP 800-53, ISO/IEC 42001, MITRE ATLAS, OWASP LLM Top 10, CIS Controls, CSA AICM 1.1, SOC 2, and related sources into a single operating model for teams that need both technical depth and auditability.

The framework exists because AI-enabled systems, especially those with tool access and autonomous action-taking capability, create governance and control gaps that are not fully resolved when organizations apply traditional cyber controls without AI-specific interpretation. ARMCF addresses that gap by defining six lifecycle-aligned domains, mapping them to recognized frameworks, and translating them into practical control objectives, risk scenarios, and implementation steps that security and risk leaders can operationalize.

Industry Resources For Practitioners

ARMCF Downloadable Framework

For technical leaders, ARMCF offers a common language for risk identification, policy design, control selection, security architecture, detection engineering, and incident response. For non-specialist readers, its core message is simpler: organizations should not adopt AI and agentic systems at speed unless they can identify what they use, define who is accountable, constrain what AI can access, observe how it behaves, and recover safely when things go wrong.

Scope Definition

ARMCF applies to AI systems with autonomous decision-making or action-taking capability, including LLM-backed applications, orchestrated multi-agent pipelines, tool-using agent workflows, and AI-assisted human workflows where AI model output materially influences decisions. It covers systems that are built internally, integrated through APIs, procured as AI-enabled SaaS, or introduced indirectly through embedded features in third-party products.

The most material organizational risk often comes less from the model itself than from the operating environment around it: context access, tool invocation, external dependencies, delegated permissions, and weak governance. ARMCF therefore treats AI as both a technology risk and an operating model risk, which makes it useful for security architecture, third-party governance, internal audit, compliance, and resilience planning.

In practice, ARMCF applies to any AI capability that can influence meaningful business outcomes, access sensitive data, trigger downstream actions, or create accountability exposure for the organization. Read-only summarization tools may require lighter governance, while fully autonomous systems require a much broader set of controls and oversight.

Driving Factors

ARMCF is driven by the need to bring governance, security, threat modeling, resilience, and auditability into one operating model rather than treating them as separate workstreams.

This design reflects the reality that organizations are adopting AI faster than standards, procurement processes, and assurance models can fully adapt to.

One major driver is the fragmentation of existing guidance. Traditional cybersecurity frameworks provide strong foundations for control design and monitoring, but they do not always express risks such as prompt injection, insecure tool orchestration, agent identity misuse, data leakage, or autonomous overreach in language that operational teams can directly act on. AI governance frameworks, meanwhile, often describe principles well but may stop short of detailed security enforcement or SOC integration.

A second driver is the emergence of agentic systems as a different risk class. ARMCF explicitly addresses tool-calling agents, multi-step planning systems, unsupervised execution patterns, model supply chains, Model Context Protocol (MCP) style brokered tool access, and identity delegation concerns because those elements expand the potential blast radius of compromise beyond a single model. In practical terms, an AI system that can send email, modify files, call APIs, or execute workflow steps must be governed more like a privileged digital actor than like a static software component.

A third driver is the need for regulated organizations to preserve auditability. Security and risk leaders in financial services, healthcare, public sector, and critical infrastructure environments need traceable ownership, documented risk acceptance, evidence preservation, and cross-mapping to recognized standards before AI adoption can scale safely. ARMCF responds by embedding accountability, logging, classification, evidence handling, and framework mappings into its structure rather than leaving them as afterthoughts.

“An AI system that can send email, modify files, call APIs, or execute workflow steps must be governed more like a privileged digital actor than like a static software component.”

Aim and Purpose

ARMCF provides a governance and risk layer that sits above the technical control stack and helps organizations that are building, deploying, or procuring AI and agentic systems to operate them safely and consistently. Its purpose is to translate AI and agentic risks into implementable management expectations, technical safeguards, monitoring requirements, and recovery processes.

At a strategic level, the framework helps leadership define acceptable autonomy, prohibited use cases, risk ownership, and the minimum control posture expected before production deployment. At an operational level, it helps engineering, security, and risk teams discover AI assets, model threats, constrain permissions, monitor behavior, handle incidents, and improve controls over time.

The practical value of this dual aim is that ARMCF supports both governance conversations in the boardroom and design conversations in architecture review forums. It gives senior managers a structure for accountability and readiness, while giving practitioners a method for assessing their environments and prioritizing remediation.

Core principles

Figure 1: ARMCF Core Principles

ARMCF is built around five core principles: accountability, proportionality, lifecycle coverage, security-by-design, and auditability.

1. Accountability: every production AI system should have a named owner, a defined risk function, and a clear RACI for lifecycle decisions such as procurement, deployment, monitoring, and decommissioning. Without that discipline, organizations cannot reliably determine who is authorized to accept AI risk, approve change, or respond to incidents.

2. Proportionality: ARMCF recognizes that not all AI systems present equal risk and explicitly supports classification by risk tier, autonomy level, data sensitivity, and use case criticality. This enables organizations to apply stronger controls to high-impact or agentic systems without burdening low-risk use cases with the same level of friction.

3. Lifecycle Coverage: ARMCF is structured around six domains—GOVERN, IDENTIFY, PROTECT, DETECT, RESPOND, and RECOVER—which together reflect the full operational lifecycle of secure AI adoption. Meaningful readiness requires movement from policy to implementation, to monitoring, to response, to recovery in a coherent and repeatable way that uses principles and pillars familiar to other security frameworks.

4. Security-by-Design for Agentic Systems: Prompt inspection, tool allowlisting, agent identity, data minimization, software supply chain integrity, and behavioral monitoring appear in the framework as baseline control concepts because AI risk emerges from system interaction, not only from model content. In simple terms, ARMCF assumes that good intentions and acceptable use policies are not enough without technical enforcement and observable evidence.

5. Auditability: The framework repeatedly emphasizes structured inventories, risk scoring, evidence preservation, SIEM export, sign-off, review dates, and roadmap milestones because mature AI governance must be inspectable and defensible under scrutiny. That orientation makes the framework especially relevant to organizations with internal audit, regulatory, or customer assurance obligations.

“ARMCF assumes that good intentions and acceptable use policies are not enough without technical enforcement and observable evidence.”

Figure 2: ARMCF Lifecycle

Pillars and Domains

ARMCF is organized into six domains that act as the framework’s operational pillars.

1. GOVERN: Establishes accountability, policy, risk appetite, inventory discipline, third-party governance, and fairness expectations.

2. IDENTIFY: Covers asset discovery, data-flow understanding, AI-specific threat modeling, risk scoring, and blast-radius analysis.

3. PROTECT: Defines preventive controls across prompts, outputs, tools, agent identity, least-privilege data handling, secure development, and workload isolation.

4. DETECT: Focuses on telemetry, behavioral baselines, anomaly detection, data exfiltration monitoring, SIEM integration, and supply chain monitoring.

5. RESPOND: Defines incident classification, containment, evidence handling, escalation, and root-cause analysis for AI-specific events.

6. RECOVER: Addresses restoration, re-validation, control improvement, and lessons learned.

These pillars are meaningful because they translate strategic intent into operational sequencing. For example, an organization that has drafted an AI policy but lacks inventory, monitoring, or containment capability cannot credibly claim readiness for high-risk agentic deployment. ARMCF therefore encourages leaders to think in terms of control completeness rather than isolated compliance artifacts.

Figure 3: ARMCF Journey Stages

Foundational Guidance

For security and risk management leaders, ARMCF offers several foundational guidance steps.

1. Establish policy and risk appetite before broad enablement.
   Organizations should define acceptable automation levels, prohibited use cases, approved providers, and decision rights before business units scale adoption through pilots or embedded vendor features.

2. Maintain a live AI system registry.
   The registry should record system name, owner, use case, autonomy level, data sensitivity, deployment environment, and control-layer coverage.

3. Model threats in AI-native terms.
   Traditional threat modeling remains useful, but ARMCF calls for coverage of prompt injection, insecure output handling, training data poisoning, denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure tool design, excessive agency, automation bias, model theft, adversarial examples, and backdoored models.

4. Enforce least privilege and observable control at each decision point.
   Agent tool access, agent identities, secrets injection, network reachability, and policy guardrails should be constrained and logged.

5. Treat AI monitoring and incident response as first-class SecOps concerns.
   AI telemetry should flow to the SIEM, alert categories should be tuned for AI-specific attack chains, and response procedures should support kill-switch, quarantine, forensic preservation, and root-cause analysis.

Security Leadership Use Cases

ARMCF is especially useful for leaders who must align innovation with control assurance in regulated or audit-heavy environments. A CISO or security architect can use the framework to define minimum production requirements for AI systems, enforce role ownership, and prioritize investments in identity, data protection, prompt security, and monitoring.

A security risk manager can use ARMCF to standardize risk statements, map threats to controls, document residual risk, and record review cycles using the framework’s risk-register model. An internal audit or compliance function can use the cross-mapping to NIST, ISO, CIS, SOC 2, and EU AI Act-style tiers to evaluate whether AI governance claims are supported by documented processes and evidence with measurable metrics.

ARMCF gives stakeholders a shared operating model: leaders define acceptable risk, practitioners implement controls, and assurance functions verify whether those controls exist and work.

Readiness Model

ARMCF can also be read as a readiness model for AI and agentic adoption. At the most basic level, organizations need visibility: a current inventory, classification, data-flow mapping, and identification of tools and dependencies. Without that baseline, later controls become difficult to scope or test.

The next level is governance and prevention, where policy, ownership, tool allowlists, agent identities, data minimization, network isolation, and secure build controls are introduced.

After that comes operational maturity, where behavioral monitoring, prompt anomaly detection, SIEM integration, incident playbooks, and evidence preservation are embedded into daily operations.

The highest level is continuous improvement. Recovery planning, quarterly risk review, post-incident control enhancements, supply chain monitoring, and board-level reporting create the feedback loops required for sustainable governance. This staged interpretation aligns well with the implementation roadmap described in the core framework, which moves from foundation to enforcement to maturity over a defined period.

Figure 4: ARMCF Framework Mappings

Mapping and Assurance

One of ARMCF’s strengths is that it does not ask organizations to abandon familiar frameworks. Instead, it acts as a translation layer that maps AI-specific needs to known governance and security structures such as NIST AI RMF, NIST SP 800-53, ISO 42001, CIS Controls, SOC 2, MITRE ATLAS, OWASP LLM Top 10, and CSA AICM 1.1. In regulated settings, assurance depends on demonstrating continuity with recognized models rather than introducing entirely isolated methodologies.

The control mapping in the core framework spreadsheet that accompanies this paper shows how ARMCF controls connect to source obligations. Examples include linking GV-1 to NIST AI RMF GOVERN and NIST 800-53 risk management controls, PT-1 to NIST 800-53 input validation and transmission confidentiality, PT-3 to identity and access enforcement, DT-4 to SecOps monitoring, and RC-4 to continuous improvement requirements. These mappings help practitioners explain why a given control matters not only for AI safety but also for governance, audit, and assurance alignment. For each control, the mapping spreadsheet also provides a suggested measurable metric along with scoring guidance as examples of how a control can be defined, applied and then consistently measured.

ARMCF also incorporates EU AI Act-style risk tiering concepts, distinguishing unacceptable, high, limited, and minimal risk use cases and tying them to varying expectations for domain coverage and oversight. A tiered model supports a defensible management narrative: stronger autonomy and stronger consequence require stronger governance. While full EU AI Act compliance has recently been postponed until 2027/28, it is still important to move ahead with preparations for compliance and to design new AI and agentic systems with the relevant controls in place.

“Stronger autonomy and stronger consequence require stronger governance”

Implementation Guidance

The framework’s implementation roadmap provides a practical starting point for leaders who need to move from concept to delivery. In the foundation phase, the emphasis is on inventory, risk appetite, ownership assignment, threat modeling for the highest-risk systems, prompt inspection, and tool allowlisting. These are sensible first steps because they establish visibility and the earliest preventive boundaries.

In the enforcement phase, the framework introduces stronger identity, monitoring, SIEM correlation, incident playbooks, and AI SBOM integration. This reflects a transition from governance intention to control durability, where organizations begin to instrument AI systems in the same manner they instrument privileged workloads and critical applications.

In the maturity phase, the roadmap calls for broader MITRE ATLAS detection coverage, formal risk assessment of in-scope systems, tabletop response exercises, quarterly governance review, board reporting, and evaluation against ISO 42001 or other certification readiness. That final stage is especially relevant for organizations that must demonstrate not just intent, but measurable operational competence over time.

Putting ARMCF into Practice

For executive and non-specialist readers, the simplest interpretation of ARMCF is that AI adoption requires the same seriousness organizations already apply to identity, cloud, and third-party risk. Before granting AI systems broad access or autonomy, leadership should know what systems exist, what they can do, what data they can reach, who owns them, and how they are stopped when they misbehave.

This message does not imply that AI should be slowed indefinitely. It implies that adoption should be staged, classified, and evidenced in proportion to risk. The stronger the autonomy and the higher the consequence, the stronger the governance, technical controls, and monitoring that should be expected.

Figure 5: Control Maturity Versus Risk Exposure

Closing Analysis

ARMCF is valuable because it translates the abstract challenge of AI governance into a concrete operating framework for security and risk leaders. Its main contribution is not unique or novel, but the integration of established cyber, risk, and AI guidance into a structure that reflects how agentic systems actually create business exposure.

For practitioners, the framework’s enduring strength is its balance: it is technical enough to guide architecture, controls, and incident handling, yet structured enough to support policy, auditability, and management communication. It helps organizations assess readiness, prioritize capability development, and introduce AI and agentic systems with clearer ownership, stronger controls, and more defensible assurance outcomes.

“Organizations should not adopt AI and agentic systems at speed unless they can identify what they use, define who is accountable, constrain what AI can access, observe how it behaves, and recover safely when things go wrong.”

Accompanying Resources:

SACR has provided some sample tools and adjuncts to this document that provide additional detail of the recommended controls to implement. These are laid out in the same domains and principles as the guidance sections above, and also contain mapping to popular AI, Cloud and Security frameworks as well as some suggested metrics that can be used to measure compliance with a given control.

These resources are provided for informational purposes only. They do not constitute legal, compliance, or implementation advice, and SACR does not guarantee their accuracy, completeness, or suitability for regulated environments. Organizations should validate any control mapping, metric, or implementation decision against their own risk, legal, and compliance requirements.

Please also read the FAQ for further explanation when considering risk and compliance implications.

Industry Resources For Practitioners

Full ARMCF Guide & Resources

ARMCF Control Assessment Tables

The following summary tables adapt the attached ARMCF domains and control objectives into a practitioner-friendly methodology reference set for self-assessment and readiness analysis. Teams can use them to review existing controls, identify gaps, and prioritize remediation in advance of broader AI and agentic rollout.

In the accompanying Control and Mapping Spreadsheet for ARMCF, the same domains are represented in more detail, with accompanying cross-references to existing Security frameworks, and with suggested measurement metrics and sample scoring rubrics also provided.

Domain Overview Table

The six ARMCF domains should be treated as an interconnected operating lifecycle, not as separate compliance categories. GOVERN and IDENTIFY establish the foundation; PROTECT and DETECT create enforceable control and visibility; RESPOND and RECOVER determine whether the organization can contain failure and restore operations safely. An organization’s readiness is ultimately limited by its weakest material control, not its average maturity score.

GOVERN Methodology

The GOVERN domain establishes the authority under which AI risk is accepted and managed. Before evaluating technical safeguards, the organization should be able to identify each production AI system, its accountable owner, its permitted use, its autonomy level and the individual authorized to accept residual risk. A policy without ownership, inventory and decision rights provides limited assurance.

IDENTIFY Methodology

The IDENTIFY domain determines whether the organization understands its actual AI exposure. This includes sanctioned and shadow AI, data and context flows, reachable tools, external dependencies and the maximum potential blast radius. An assessment should consider the complete operating environment around the model, not only the model itself.

PROTECT Methodology

The PROTECT domain translates governance decisions into enforceable technical boundaries. Agent identities, tool invocation, data access, secrets, network reachability and software supply-chain integrity should be restricted by default and expanded only through deliberate approval. For agentic systems, least privilege must apply to both information access and action-taking capability.

RESPOND Methodology

The RESPOND domain tests whether the organization can contain an AI-related incident without depending on the affected system to cooperate. Response procedures should support rapid credential revocation, tool-access suspension, agent quarantine, kill-switch activation, evidence preservation and escalation to the appropriate security, legal, privacy and business stakeholders.

RECOVER Methodology

The RECOVER domain addresses the conditions required for safe restoration. Restarting an AI system is not sufficient. Before redeployment, teams should verify the model and supporting artifacts, identities, permissions, allowlists, dependencies, data integrity and behavioral baselines. Material findings should feed back into the risk register and control roadmap.

Risk Register Reference

The risk register connects credible AI risk scenarios to the controls intended to prevent, detect or contain them. The examples below are illustrative rather than exhaustive. Organizations should adapt them to their own systems and record affected assets, likelihood, impact, control effectiveness, residual risk, accountable owner, remediation actions and review dates.

Conclusion & Recommended Next Steps

ARMCF gives security and risk leaders a practical structure for doing that. It connects governance decisions to technical safeguards, observable evidence, incident response and recovery. It also gives CISOs, engineering teams, risk functions and auditors a shared language for determining whether an AI system is ready for production and what must improve before it receives broader access or authority.

Security leaders can begin applying ARMCF through five practical actions:

1. Inventory sanctioned, embedded and shadow AI systems across the organization.

2. Classify each system by autonomy, data sensitivity, business criticality and potential consequence.

3. Assess the highest-risk systems against the six ARMCF domains.

4. Prioritize material gaps in identity, tool access, data protection, monitoring, containment and recovery.

5. Report readiness, residual risk and remediation ownership to the appropriate executive and governance stakeholders.

Begin with the systems that can access sensitive data, invoke tools, call APIs, modify records or initiate consequential business actions. These systems represent the most immediate need for defensible governance and control.

Put ARMCF into Practice

Access the accompanying ARMCF resources to begin assessing your environment:

• Download the ARMCF Control and Mapping Spreadsheet

• Review the full ARMCF presentation and implementation guide

• Use the domain assessment tables to identify control gaps

• Consult the ARMCF FAQ for additional risk and compliance context

We welcome feedback from CISOs, security architects, AI security practitioners, risk leaders and auditors applying ARMCF in real environments. Your practical experience will help us strengthen future versions of the framework as agentic architectures and security requirements continue to evolve.

Review the framework. Assess one high-risk AI system. Identify the control gaps that must be addressed before its autonomy expands.

To receive future SACR research, framework updates and practitioner guidance, subscribe to Software Analyst Cyber Research and share ARMCF with the leaders responsible for AI governance and security in your organization.

Questions or practitioner feedback? Contact the SACR research team using the contact form.

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Executive Summary: The Evolution of Ransomware

For a decade, defeating ransomware meant one thing: have backups you can restore. Attackers have since moved the fight. Instead of your data, they now target your recovery, disabling the consoles and credentials behind your backups before the ransom note ever lands. This report introduces Ephemeral Ransomware Resilience (ERR), a recovery standard that holds even when the attacker already has the keys.

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Ransomware taught the security industry a hard lesson, and the industry learned it well. After years of watching organizations pay to unlock their own files, defenders invested in backups, then in immutable backups, and built recovery plans designed to make the ransom irrelevant. The logic was sound: if you can restore, you do not have to pay.

Attackers adapted to that logic. If reliable backups are what defeat ransomware, then the backups themselves become the target. Today’s operators rarely start with your data. They start with your recovery. During the weeks they spend inside a network before anyone notices, they hunt for the systems that would let you bounce back: the backup console, the credentials that control it, the replication settings, the restore workflows. They weaken or destroy that infrastructure first. By the time the ransom note appears, the recovery you were counting on may already be gone.

This is the part that catches even prepared teams off guard. Storage immutability was supposed to close this door. But write protection only stops deletion from inside the console. The console itself, along with the API and command line behind it, stays reachable. An attacker holding valid administrative credentials does not need to break anything. They use the access your own organization granted them, and copies that cannot be edited can still be expired, unprotected, or cut off from the systems meant to restore them.

SACR defines the response to this problem as Ephemeral Ransomware Resilience, or ERR: an architectural standard for recovery that stays intact even when an attacker already holds administrative control. ERR rests on three ideas, each aimed at removing a single point of failure. First, isolate the management plane, so protected copies cannot be reached through any console, API, or command line, no matter whose credentials are compromised. Second, create clean copies automatically and continuously, so a recent, trustworthy recovery point always exists without depending on a person acting correctly at the worst possible moment. Third, keep a temporary, independent compute environment on standby, so the business can keep running while primary systems are rebuilt.

For security leaders, the shift is simple to state and significant in practice. Ransomware resilience should be measured by provable recoverability, not by the existence of backups. The right question is no longer whether you have copies, but whether you could prove you would still recover if an attacker already held the keys. The pages that follow trace how the threat evolved, why tape, cloud immutability, and offsite replication all fall short against a credentialed attacker, and the specific questions every team should be able to answer about its own recovery architecture.

Recovery has become part of security architecture. ERR offers a practical way to know whether yours can withstand the attack it was built to survive.

Key Insights: The New Adversarial Reality

• Management-Plane Vulnerability: The $4.44M average breach cost is driven by 24-day attacker dwell times, allowing total compromise of reachable backup infrastructure before detection.

• Industrialized Handoff: The window from initial access to ransomware deployment has collapsed from 8 hours to 22 seconds, making human-led response obsolete.

• Structural Gaps: Current backup vendors protect storage while leaving control paths exposed

Implication: CISOs should treat management-plane isolation, ephemeral compute continuity, and on-demand rebuildability as architectural requirements rather than product features. Modern attacker timelines and AI agent-based attacks can routinely outrun human response. ERR assumes compromise is inevitable. Its objective is to make environments rebuildable on demand from trusted, automated sources while continuously proving restore readiness. This is the true promise and underpinning of becoming a resilient enterprise.

Strategic Mandates for CISOs

1. Enforce Isolation: Mandate management-plane isolation, not just storage immutability, as a non-negotiable procurement baseline.

2. Verify Continuity: Validate sub-hourly recovery points and ensure ephemeral compute exists to sustain a 30-day restoration window.

3. Scrub Restore Points: Implement pre-restore threat scanning to eliminate attacker persistence and prevent reinfection loops.

The Ransomware Threat Landscape: From Encryption to Management-Plane Sovereignty

Recovery infrastructure is now a primary target. Attackers have pivoted from encrypting production data to dismantling the recovery control planes intended to safeguard it. Adversarial innovation has moved from volume-based encryption to industrialized, high-speed management-plane compromise, targeting the very consoles and APIs used for restoration. Effective defense requires architectural isolation that assumes administrative compromise, shifting the goal from preventing encryption to ensuring environment rebuildability.

1. Phase 1: Encrypt-and-Ransom (2013–2018). Primary focus on availability via production data encryption. Organizations neutralized this model through basic offline backups, forcing an adversarial pivot.

2. Phase 2: Backup-Targeting (2019–2023). Pre-detonation dwell time utilized to locate and delete repositories. Backup destruction became routine, steadily declining the efficacy of traditional recovery methods.

3. Phase 3: Management-Plane Sovereignty (2024–Present). Attackers leverage compromised admin consoles to delete backups on demand. The handoff from initial access to detonation has collapsed to 22 seconds, making human-led response impossible.

Strategic Evolution: The Industrialization of Extortion

The Escalation of Extortion Models

Encryption is no longer the sole lever. Attackers now layer extortion models to bypass recovery defenses and maximize financial pressure through data suppression. The rise of data-theft-only variants (increasing from 2% in 2020 to >15% in 2025¹) and RaaS-driven industrialization has outpaced traditional perimeter and backup security. CISOs must adopt the Ephemeral Ransomware Resilience (ERR) standard to sustain operations via management-plane isolation and continuous, automated validation.

• Triple extortion (third-party pressure): Attackers contact the victim’s customers, partners, or regulators directly, threatening to release their data or notify authorities, amplifying pressure beyond the primary victim.

• Data-theft-only extortion (no encryption): A rapidly growing variant, rising from roughly 2% of financially motivated incidents in 2020 to more than 15% in 2025 (Google M-Trends 2026). Attackers skip encryption entirely, relying purely on data exposure threats. This model is faster, lower-risk for the attacker, and bypasses backup-based defenses completely.

• Ransomware-as-a-Service (RaaS): The criminal supply chain that made all of the above scalable. Developers lease ransomware toolkits to affiliates who execute attacks and split proceeds. LockBit, BlackCat/ALPHV, Clop, and Akira all operated this model. Even after law enforcement disrupted LockBit and BlackCat in 2024, new affiliate groups emerged rapidly, with Flashpoint tracking a 179% year-over-year surge in attacks from successor groups.

Typical Ransomware Attack on Backups

Full Ransomware Attack Phases in Detail

1. Initial Access: This is the entry point into the target environment. Threat actors typically rely on the paths of least resistance to gain their initial foothold.

   • Phishing: Sending malicious attachments or links to harvest credentials or deploy initial loaders.

   • Exploitation of Public-Facing Applications: Targeting unpatched vulnerabilities in firewalls, VPNs, or web servers.

   • Compromised Credentials: Utilizing credentials purchased from Initial Access Brokers (IABs) or obtained through brute-force attacks on exposed Remote Desktop Protocol (RDP) instances.

2. Execution and Persistence: Once inside, the attacker needs to ensure they maintain access even if the initial entry point is discovered, patched, or rebooted.

   • Command and Control (C2): Establishing communication channels between the compromised network and attacker-controlled servers (often using tools like Cobalt Strike or Sliver).

   • Persistence Mechanisms: Creating scheduled tasks, modifying registry run keys, or creating new system accounts to ensure continuous access.

   • Defense Evasion: Disabling endpoint detection and response (EDR) tools, clearing logs, or using Living off the Land (LotL) techniques, abusing legitimate administrative tools like PowerShell or WMI to blend in with normal traffic.

3. Privilege Escalation: Initial access rarely grants the administrative rights needed to deploy ransomware network-wide. The attacker must elevate their privileges.

   • Credential Dumping: Extracting passwords or hashes from system memory (e.g., using Mimikatz to dump LSASS).

   • Active Directory Exploitation: Utilizing attacks like Kerberoasting or exploiting domain misconfigurations to gain Domain Admin privileges.

4. Discovery and Lateral Movement: With elevated privileges, the attacker maps the network to identify high-value targets, critical data repositories, and the organization’s backup infrastructure.

   • Network Reconnaissance: Scanning for open ports, shared drives, and active directory structures.

   • Lateral Movement: Moving from the initial compromised endpoint to other systems within the network using protocols like SMB or RDP, utilizing the credentials harvested in the previous stage.

5. Collection and Exfiltration: In modern double extortion campaigns, attackers steal sensitive data before locking the systems. This gives them leverage to demand payment even if the victim can restore from backups.

   • Data Staging: Archiving and compressing sensitive files into hidden directories.

   • Exfiltration: Transferring the staged data to external cloud storage providers (like MEGA or cloud sync tools) using protocols that mimic normal outbound web traffic.

6. Impact (Encryption and Extortion): The final, visible stage of the attack.

   • Backup Destruction: Locating and deleting shadow copies, neutralizing local backups, or poisoning cloud backup routines to prevent easy recovery.

   • Mass Deployment: Pushing the ransomware payload simultaneously across the network using tools like Group Policy Objects (GPO) or deployment software (e.g., SCCM).

   • Encryption: Executing the encryption routine, locking the files, and dropping the ransom notes detailing how the victim can contact the attackers to negotiate payment for the decryption key and the promise to delete the exfiltrated data.

With All These Critical Attacks, We Can See a Pattern Emerge

The pattern across all landmark incidents is that attackers did not simply encrypt files and wait. They spent dwell time understanding the victim’s backup and recovery posture, locating credentials for management consoles, and staging data exfiltration before triggering the final payload. Recovery architecture, not just perimeter defense, was a decisive factor in outcome severity and the strength of their breaches.

Modernizing Disaster Recovery: Architecture vs. Reactive Monitoring

Effective disaster recovery in the modern threat landscape requires moving beyond static, signature-based defenses. As ransomware operators weaponize shifting infrastructures, moving from encrypting data to dismantling recovery control planes, security teams must adopt proactive telemetry. By integrating anomaly detection with continuous configuration monitoring, defenders can surface architectural tampering before it manifests as a total recovery failure.

Anomaly Detection vs. Malware Scanning

These tools serve distinct, non-overlapping functions:

• Anomaly Detection: Uses machine learning to establish behavioral baselines. It flags symptomatic evidence of active compromise, such as brute-force attempts on backup consoles, changes to backup policies, or unexpected changes in backup volume, enabling response before data loss occurs.

• Malware Scanning: A foundational validation layer focused on identifying known threats and validating software integrity within production environments.

Operational Directive: Malware scanning validates the cleanliness of a binary; anomaly detection validates the integrity of the infrastructure.

Change Detection as a Fidelity Signal

In compromised environments, unauthorized infrastructure modifications are often the sole indicator of administrative takeover. Threat actors frequently compromise infrastructure to isolate administrators, modify backup permissions, or establish persistence. Because adjacent sensors are often blinded or bypassed, continuous change monitoring of the backup control plane is essential. This telemetry acts as a high-fidelity trigger for investigation, identifying when the ground truth of the environment deviates from policy.

The Adversarial Focus: Targeting the Backup Control Plane

Backup infrastructure is the primary target for modern extortion. Threat actors monetize breaches by neutralizing the recovery path before triggering ransomware, either by severing administrator access, poisoning recovery routines, or modifying permissions to facilitate silent data exfiltration. Defender priorities must shift from purely securing the storage layer to hardening the access architecture surrounding it. Any management-plane configuration, permission, API key, or administrative account that enables recovery must be treated as a potential attack vector for lateral movement or sabotage.

Comparison of Anomaly Detection vs Malware Scanning

While both tools are necessary for a layered security strategy, they serve distinct operational purposes:

Why Tape, Cloud Immutability, and Offsite Replication All Fail the Adversarial Recovery Test

Before the management-plane threat emerged, these approaches were considered adequate. Air-gapped tape offered physical separation. Cloud object lock offered write protection at the storage layer. Offsite replication offered geographic redundancy. Each was designed to protect against the operational failure modes that dominated the threat model through the 2010s: hardware failure, accidental deletion, and site-level outages. None were designed to survive a credential-holding attacker with console access and time to operate. When that threat model is applied, a structural gap appears in each approach, not in the storage medium, but in the access architecture surrounding it.

• Air-gapped tape and drive: Operationally infeasible for modern recovery SLAs; often unmaintained.

• Cloud immutability with console access: Storage write protection exists, but admin credentials can issue delete commands via the management API. If those credentials are compromised, the backup is reachable.

• Offsite replication: Duplicates the problem: if the primary management plane is compromised, the replication credential is too.

• Manual backup schedules: Introduces human failure points at exactly the moment, during incident response, when humans are most error-prone and distracted.

The gap is not in storage technology; it is in access architecture. Most backup platforms were designed for operational recovery (hardware failure, accidental deletion), not adversarial recovery (attacker with admin credentials attempting to eliminate recovery options before triggering ransomware).

The financial stakes are clear. IBM’s 2025 Cost of a Data Breach Report puts the global average breach cost at $4.44 million². Verizon’s 2025 DBIR places median attacker dwell time at 24 days for non-actor-disclosed breaches³, leaving a window wide enough for attackers to map, stage, and execute against backup infrastructure long before detonation. Chainalysis notes that while total ransomware payments stagnated in 2025, median payment sizes rose to $59,565 as gangs shifted focus to higher-value targets.⁴

Designing for adversarial recovery requires a fundamentally different assumption: treat the management plane as compromised by default.

This distinction matters because:

1. Software-layer immutability protects data from deletion within the management console, but the console itself remains reachable. A compromised admin account can still interact with the backup environment and infrastructure.

2. Architectural isolation removes the management plane entirely; there is no console, API, or CLI path to the protected copies, regardless of what credentials the attacker holds. The former is a policy control. The latter is a design guarantee. Only the latter holds under full credential compromise.

Five Questions Every CISO Should Ask Before Trusting Their DR Architecture

Most DR architectures have never been stress-tested against the adversarial threat model, the scenario where the attacker already holds valid administrative credentials and is actively attempting to eliminate recovery options before triggering ransomware. Vendor documentation and sales briefings rarely address this scenario with architectural specificity. These five questions are designed to move that conversation from marketing claims to verifiable architecture. They apply equally in initial procurement, annual DR reviews, and post-incident retrospectives. A vendor that cannot answer them with specificity not policy language, but demonstrable design, fails the ERR standard.

When auditing a DR architecture against ERR principles, five questions expose the structural gaps:

1. Can an attacker with full admin credentials delete or modify your backup copies?
   Note: If yes or if the vendor cannot demonstrate otherwise, the architecture fails the ERR management-plane test.
   

2. How often are isolated copies created, and is the process fully automated?
   Manual processes introduce failure at exactly the wrong moment. Hourly automated copies are the baseline.
   

3. What is your recovery compute strategy if primary infrastructure is completely unavailable?
   Restoring to already destroyed infrastructure is not a recovery plan. Ephemeral VM continuity is required.
   

4. Can you prove restore points are free of attacker persistence before restoration begins?
   Restoring a reinfected image extends the incident. Pre-restore threat scanning is an emerging best practice. Alternatively, organizations can restore data into a separate standby environment and scan it before returning it to production.
   

5. What SLA governs ephemeral continuity duration?
   30 days is a baseline. Regulated industries may require longer windows and granular RTO guarantees.
   
   

Defining Ephemeral Ransomware Resilience (ERR): The Architectural Standard Immutability Forgot

Defining Ephemeral Ransomware Resilience (ERR)

SACR Definition: Ephemeral Ransomware Resilience (ERR) and the Three Pillars That Distinguish It from Storage Immutability

Ephemeral Ransomware Resilience (ERR) is the architectural standard for disaster recovery systems that remain operationally intact when an attacker holds valid administrative credentials. It addresses the gap between storage-layer and disaster recovery immutability, which protects data at rest, and management-plane isolation, which removes the attacker’s ability to locate, delete, or corrupt backup copies and recovery infrastructure through any available control path.

Best fit: Organizations with cloud and IaC momentum and leadership support for recovery engineering and control-plane hardening.

At a practical level, ERR requires three capabilities:

• Management-plane isolation that ensures backup copies cannot be accessed through a console, API, or CLI, even under full credential compromise.

• Automated continuous protection that creates copies every hour or less without human intervention.

• Ephemeral compute continuity that provisions temporary compute environments capable of sustaining business operations for 30 days or longer.

Note: Vendor-neutral ERR Framework (can apply to any platform). This section is vendor-agnostic. Any provider that can prove management-plane isolation, automated continuous protection, and ephemeral compute continuity can theoretically satisfy ERR. ERR complements detection and incident response; it is not a replacement. ERR is also not a synonym for storage-layer immutability: basic write-once/object-lock capabilities that still leave console/API/CLI control paths reachable do not meet the ERR standard. ERR assumes an attacker may already hold valid administrative credentials and focuses on ensuring recovery remains viable under that condition. Organizations that treat air-gapped backups as equivalent to ERR should verify whether their air-gap extends to the management plane, not only the storage medium.

ERR Key Outcome: Ephemeral Compute Continuity

What Is Ephemeral Compute Continuity?

Ephemeral Compute Continuity is a core requirement of the Ephemeral Ransomware Resilience (ERR) framework. It ensures that if your primary infrastructure is destroyed or compromised, you have a resilient, temporary, independent cloud environment ready to provision and sustain your business operations for a defined recovery window (30 days or more). Unlike traditional restoration, which often depends on the same infrastructure that may have been compromised, ephemeral compute provides a dedicated, standby environment to ensure your operations can continue without relying on the health of your primary systems.

Market Timing: ERR Is Early-Stage but Validated Before Becoming a Compliance Requirement

The window for establishing ERR as a recognized architectural standard is open, but it will not remain open indefinitely. Regulatory bodies are tightening DR requirements, cyber insurance underwriters are beginning to ask questions about management-plane access controls, and security leadership is increasingly aware that console-based backup deletion is a documented attacker tactic. The vendors and frameworks that define the category now will shape how buyers evaluate DR architecture for the next several years. ERR is still in its early adoption phase, giving early advocates disproportionate influence over the market narrative. As the concept matures, it will likely become a procurement checklist item, and enterprise pricing will play a larger role in buying decisions. N-able, the subject of this research report, offers flexibility and often dominates the conversation.

• Stage: Signal and Early Traction (language is forming faster than standardized requirements)

• Vendor signals: N-able Cove Data Protection represents an early MSP-accessible path toward full ERR alignment; in parallel, enterprise-tier platforms are increasingly emphasizing management-plane isolation and verified recovery (including pre-restore validation) as roadmap priorities

• Customer signals: Growing CISO interest in whether an attacker with compromised credentials could delete backups. This is a question that most incumbent platforms cannot answer with architectural certainty.

• Regulatory signals: DORA (EU financial sector DR requirements), NIST CSF 2.0 recovery function emphasis, and tightening cyber insurance requirements are creating compliance demand for demonstrable recovery architecture

• SACR verdict: Real problem, early but credible solutions, market language still forming but ideal timing for concept evangelism, and for MSP-delivered ERR to establish the category before enterprise pricing dominates

Common Misconceptions: Why Storage Immutability, Air-Gaps, and EDR Are Not Substitutes for ERR

Because ERR is a newly coined framework, buyers and vendors alike are still working out what it does mean, and what it does not. Several common misreadings create dangerous gaps in disaster recovery, or DR, posture. Organizations should not confuse ERR with storage immutability, traditional vault architectures, or detection technologies. They should verify that any claimed air gap extends to the management plane rather than only the storage layer. Each misconception leads organizations to believe they have adversarial recovery coverage that they do not, in fact, have.

• ERR is not storage immutability alone; write protection without management-plane isolation fails the adversarial test.

• ERR is not a product category; it is an architectural standard that multiple vendors may satisfy through different implementations.

• ERR is not a replacement for EDR/XDR/UEM/MDR; it is the recovery-layer complement to detection and response.

Solution Landscape: Mapping Recovery Architectures to ERR

This taxonomy categorizes recovery stacks by their resilience to administrative compromise, differentiating between marketing claims of immutability and the architectural reality of control-plane isolation.

• Immutable Storage Primitives (Hyperscalers): Write Once, Read Many (WORM) and object-lock policies harden the data layer but fail to mitigate console or API risk. Without management-plane severance, these remain reachable targets for credentialed attackers.

• Traditional Backup Platforms: Structurally vulnerable to credential-holding attackers. These are optimized for accidental deletion, not adversarial takeover, and fail if the management plane remains accessible via standard administrative paths.

• Enterprise Vault / Isolated Recovery Environments: Architecturally robust, providing hardened silos for data. However, they often impose prohibitive costs and operational overhead, limiting adoption to high-end enterprise deployments.

• DRaaS & Orchestration Platforms: Superior in recovery speed and automation, yet frequently lack default management-plane isolation. Resilience here is a matter of configuration rather than fundamental design.

• Verified Recovery / Clean-Room Testing: Represents the market pivot from simple immutability to provable recoverability. Pre-restore validation is now a baseline expectation for mature recovery stacks.

SACR Directive: ERR is an architectural standard, not a brand. Any platform satisfying the three pillars, management-plane isolation, continuous automated copies, and ephemeral compute continuity, meets the standard. The adversarial threat model renders legacy designs relying on reachable consoles obsolete.

Benchmarking Recovery Strategies

Modernizing DR Validation: Continuous Resilience vs. Manual Testing

Traditional disaster recovery testing relies on manual, point-in-time exercises that are often infrequent and prone to human error. Modern resilient architectures mandate a shift to continuous, automated validation, ensuring that recovery capabilities are not merely planned, but provably operational.

Automated Recovery Testing (ART): Proving Viability

ART replaces manual, infrequent testing cycles with software-driven validation, continuously verifying that backup data is not just present, but functional and ready for deployment.

• Orchestrated Sandboxing: Automated instantiation of isolated environments (sandboxes) enabling testing without disrupting production.

• Application-Level Validation: Testing extends beyond simple data existence; the system boots virtual machines or databases and executes validation scripts to confirm application-layer health.

• Compliance and Reporting: Automated teardown generates verifiable telemetry, which includes Recovery Time Objective (RTO) metrics, which are key to providing objective evidence of readiness for auditors and stakeholders.

Clean Room Recovery (CRR): Ensuring Integrity

Standard restores risk a catastrophic re-infection loop, where dormant malware, backdoors, or compromised credentials within backups trigger immediate re-compromise. Clean Room Recovery provides the sanitization layer necessary to decouple recovery from potential persistence.

• Isolated Sanitization: Systems are restored into a sterile, walled-off environment, completely segmented from the infected production network and external internet.

• Forensic Scrubbing: Within this environment, security teams leverage Endpoint Detection and Response (EDR) and forensic tools to identify and purge persistence mechanisms before reintroduction.

• Staged Reintroduction: Only after a system is cryptographically certified as clean and patched is it moved back into the production environment.

Strategic Synthesis: Validation vs. Sanitization

These disciplines form a sequential, interdependent workflow:

1. ART (Validation) verifies that the infrastructure is capable of restoration.

2. CRR (Sanitization) ensures the data being restored is safe to operate.

A modern recovery strategy fails without both: ART guarantees your engine starts, while CRR ensures the vehicle is not sabotaged before you begin the journey.

N-able Cove Data Protection: Architectural Assessment

N-able Cove Data Protection operates as an ERR-aligned platform. Unlike legacy backup tools retrofitted for security, Cove’s design prioritizes management-plane isolation as a foundational requirement.

ERR Pillar Mapping: A Technical Audit of Cove Data Protection

• Management-Plane Isolation (Fortified Copies): These backups are stored in a physically and logically isolated environment. Access is strictly severed from the standard management console, API, and CLI. There is no automated path for an attacker, even one with full domain admin credentials, to locate, alter, or prematurely expire these copies. Restoration requires a verified, out-of-band support process, effectively functioning as an air-gap by design.

• Automated Continuous Protection: TrueDelta technology enables granular, byte-level tracking, supporting recovery point objectives (RPOs) of one hour or less. This automation removes the human dependency that typically causes backup failure during the high-stress, fog of war phase of a ransomware incident.

• Ephemeral Compute Continuity (DRaaS): Cove’s Disaster Recovery as a Service (DRaaS) provides the ephemeral compute tier necessary to sustain operations. By pre-staging standby images in the cloud, the platform allows for rapid failover, decoupling business continuity from the availability of primary infrastructure.

Analyst Directive: The MSP delivery model acts as a distribution moat. While enterprise-tier platforms achieve ERR through complex, high-overhead vaulting, N-able delivers comparable isolation via a multi-tenant channel. This simplifies the procurement and deployment of architectural resilience for mid-market organizations that lack the capacity to architect it internally.

Market Outlook: ERR as a Procurement Standard

Ephemeral Ransomware Resilience (ERR) is rapidly transitioning from a visionary architectural concept to a non-negotiable market requirement. As cyber insurance underwriters and regulatory bodies, such as DORA in the EU financial sector and NIST CSF 2.0, tighten recovery standards, simple immutability is no longer recognized as a sufficient defense against modern adversarial tactics.

Market Map: Ransomware Resilience Solutions & ERR

The Roadmap to Mainstream Adoption

• Procurement Shift (12–18 Months): Expect CISOs to move beyond generic ‘backup’ requirements, demanding specific documentation on management-plane isolation during RFP processes. Legacy platforms that rely on reachable administrative consoles will face immediate scrutiny as organizations prioritize designs where even full credential compromise cannot result in backup deletion.

• Validation as a Service: Automated Recovery Testing (ART) will become a default baseline. Proving that backups are functional and capable of booting virtual machines or databases, not just present on disk, will shift from a nice-to-have feature to an auditable necessity for business continuity.

• The Sanitization Layer: Clean Room Recovery (CRR) will define the next maturity curve. The industry is moving toward a standard where restored data must be cryptographically certified as clean within isolated environments before reintroduction into the production network to prevent reinfection loops from dormant malware.

• Channel-Led Scaling: Mid-market organizations, lacking the internal capacity for complex recovery engineering, will rely on Managed Service Providers (MSPs) to operationalize ERR. The providers that successfully abstract the complexity of management-plane isolation and ephemeral compute will capture the bulk of this emerging market by providing enterprise-grade resilience at a mid-market price point.

Strategic Cybersecurity and Disaster Recovery Industry Implications

The industrialization of extortion, where the median attacker dwell time remains around 24 days, allows threat actors significant time to dismantle recovery control planes. The shift toward ERR represents a fundamental change in the security contract: organizations are no longer just buying storage; they are procuring a guaranteed, isolated path to environment rebuildability that remains intact even when the primary infrastructure is totally compromised.

SACR Key Takeaway

Ephemeral Ransomware Resilience (ERR) corrects from potentially catastrophic outcomes by pivoting from reactive storage-level protection to proactive, architectural isolation. ERR is an essential implementation framework for modern adversarial resilience and recovery. ERR shifts the focus from simply preventing encryption to ensuring guaranteed environment rebuildability and data integrity, even in scenarios of full credential compromise.

Sources:

1. Google / Mandiant M-Trends 2026
   Median time between initial access and handoff to ransomware operators collapsed from more than 8 hours in 2022 to just 22 seconds in 2025, driven by pre-staged tooling and industrialized initial access broker collaboration. Attacks combining encryption and data theft rose to 77% of ransomware intrusions (up from 57% in 2024). Data-theft-only extortion grew from ~2% of financially motivated incidents in 2020 to more than 15% in 2025.
   https://cloud.google.com/blog/topics/threat-intelligence/m-trends-2026

2. IBM Cost of a Data Breach Report 2025
   Global average cost of a data breach: $4.44 million. Organizations using AI-driven security cut breach lifecycle by 80 days and saved ~$1.9M on average.
   https://www.ibm.com/reports/data-breach
   

3. Verizon 2025 Data Breach Investigations Report (DBIR)
   Median attacker dwell time for non-actor-disclosed breaches: 24 days. 54% of ransomware victims had their domains present in infostealer logs. 64% of victims refused to pay ransom in 2024.
   https://www.verizon.com/business/resources/T16f/reports/2025-dbir-data-breach-investigations-report.pdf
   

4. Chainalysis 2026 Crypto Crime Report: Ransomware
   Total ransomware payments stagnated in 2025 even as attacks increased. Median ransom payment rose to $59,565 (up from $12,738 in 2024), reflecting a shift toward higher-value targets.
   https://www.chainalysis.com/blog/crypto-ransomware-2026/
   

Disclosure: This report is commissioned and sponsored by N-able. SACR retains full editorial control, independence and objectivity. N-able’s role is limited to providing briefing access, customer contacts, and factual review. Sponsorship does not influence SACR’s findings, competitive analysis, or recommendations.

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Executive Summary

Email is still the cheapest, highest-leverage path into the enterprise. Three decades of layered defenses have not narrowed the vector. They have moved it up the stack. The fight today is not really about malicious attachments at the perimeter. It is about trust, identity, and human judgment inside the inbox, and across the workflows that wrap around the inbox. The argument of this report is that in 2026, email security should be understood and purchased as an identity-graph problem with auditable evidence behind it, rather than as a content-filter problem with a headline detection rate on the marketing page.

Four claims sit underneath that thesis.

Email security is becoming more contextual. Many of today’s most damaging attacks, including business email compromise (BEC), vendor email compromise (VEC), account takeover (ATO), and OAuth abuse, often contain few traditional indicators of compromise. The relevant signal increasingly lies in communication patterns, identity relationships, behavioral context, and whether a request makes sense within the normal operation of the business.

Email security is converging with identity, data, and security operations. Modern attacks rarely stop at the inbox. A phishing email may lead to credential theft, SaaS compromise, unauthorized access, or data exposure. As a result, organizations must evaluate email-security platforms based on how effectively they connect to identity systems, data-security programs, and incident-response workflows.

The market is becoming more layered. No single architecture fully addresses prevention, contextual analysis, investigation, remediation, and governance. Secure email gateways (SEGs), integrated cloud email security platforms (ICES), investigation-centric tools, and automation-driven systems coexist because they solve different parts of the problem.

The attacks are more layered. While email remains the dominant vector for social engineering, attacks are now proliferating across multiple channels and platforms such as Slack, WhatsApp, Teams, voicemail, etc. The invasion of trusted sources presents a major challenge that traditional solutions must work to solve. Until then, all messages, links, attachments, and senders must be assumed malicious.

Explainability is becoming a procurement requirement. As email security becomes more interconnected and operationally significant, organizations need to understand why a platform reached a conclusion, what evidence supports it, and how response decisions can be defended to executives, auditors, regulators, and cyber insurers.

Why This Matters

For CISOs, security architects, and senior practitioners, the practical question is not whether the secure email gateway is dead or whether ICES replaces it. The better question is what each layer uniquely contributes. Does it prevent delivery? Does it understand the communication context? Does it produce evidence? Does it reduce blast radius? Does it remediate across mailboxes, shared folders, OAuth grants, mailbox rules, and collaboration surfaces? Does it integrate into the systems where security operations teams already work?

The future of email security is unlikely to be defined by a single architecture. It will be defined by how effectively different architectures help organizations understand attacks, respond to them efficiently, and reduce risk across an ever-more-complex communication environment.

Defining Email Security

For the purposes of this report, email security refers to the technologies and processes used to protect business communications from phishing, fraud, account compromise, unauthorized access, and data exposure.

Today, email security encompasses far more than the inspection of messages, links, and attachments. Organizations increasingly evaluate communications in the context of identities, relationships, business processes, user behavior, and the infrastructure that supports modern digital workflows. As a result, email-security platforms now incorporate varying degrees of behavioral analysis, identity context, investigation, remediation, automation, and response.

The category also extends beyond the inbox itself. Many attacks involve collaboration platforms, cloud applications, messaging systems, supplier portals, and browser-based workflows as part of a broader social-engineering chain. While email remains the primary focus of this report, many vendors increasingly position their offerings as broader communication-security platforms.

For this report, email security should therefore be understood as the set of technologies and workflows used to secure business communications, the identities and relationships that support those communications, and the business processes that attackers increasingly seek to exploit.

Why Email Security Is Changing

The evolution of email security is often described through product categories, but the deeper driver is attacker behavior. Attackers have adapted around the controls organizations deployed, and each adaptation has forced defenders to expand what email security is expected to understand.

The first generation of email security focused on malicious content and suspicious infrastructure. That made sense when attackers relied heavily on spam, malware attachments, commodity phishing kits, and newly registered domains. The defensive model was built around inspection, reputation, signatures, sandboxing, and policy enforcement.

Many of the most damaging attacks today look different. They exploit established relationships, legitimate infrastructure, identity workflows, and human decision-making under pressure. As a result, the current attack surface is not simply bad email; it is business workflow abused through email.

Business Email Compromise

Business email compromise remains one of the most damaging attack categories precisely because it often lacks the artifacts that email security tools were originally built to find. A BEC attack may impersonate an executive, finance employee, legal contact, or trusted business partner. The objective is usually to trigger a payment, redirect funds, disclose sensitive information, or approve an action that appears legitimate.

The technical content of the message may be unremarkable. There may be no malware attachment, no suspicious link, and no obviously malicious domain. The signal exists in the relationship, the timing, the request, and the deviation from normal business processes.

This is why relationship intelligence, behavioral baselining, and identity context became important. A content filter may see a normal message. A system with access to communication history and tenant behavior may see an unusual request from an unusual sender to an unusual recipient at an unusual time.

Vendor Email Compromise

Vendor email compromise extends this problem further because the attacker may operate from a legitimate supplier account. The sender is real, the domain is real, the email history is real, and the business relationship is real. The anomaly may be limited to a change in payment details, an unusual invoice, or a subtle deviation in workflow.

VEC is difficult because Sender Policy Framework (SPF), DomainKeys Identified Mail (DKIM), and Domain-based Message Authentication, Reporting, and Conformance (DMARC) may all pass. Domain reputation may be clean. The communication may be part of an existing thread. In this scenario, the security question becomes less about whether the sender can be authenticated and more about whether the request aligns with expected business behavior.

This is where ICES platforms and behaviorally informed systems gained traction. They were able to observe internal mail flow, mailbox relationships, communication cadence, and post-delivery behavior in ways that legacy gateway models often could not.

Trusted-Cloud Phishing

Trusted-cloud phishing has become one of the clearest examples of why reputation-based approaches are insufficient on their own. Attackers are expanding use of Microsoft 365, Google Workspace, Adobe, Dropbox, DocuSign, Atlassian, Confluence, Power BI, Notion, Figma, Vercel, Replit, and other legitimate services to host content or facilitate phishing workflows.

The infrastructure is often legitimate. The abuse occurs inside the workflow.

This creates a structural challenge for URL-rewrite controls and reputation feeds. A trusted domain may host a malicious workflow without the domain itself being malicious. The destination may not reveal risk until the user interacts with the page, follows a redirect chain, scans a QR code, grants OAuth consent, or submits credentials.

This is why dynamic analysis, URL detonation, browser interaction, screenshot capture, and redirect-chain reconstruction are becoming more important. For trusted-cloud phishing, defenders need to understand what happens after engagement rather than only evaluating the initial destination.

AI-Generated Social Engineering

Generative AI has changed the cost structure of phishing. Highly personalized spear phishing once required manual research and careful crafting. Today, attackers can generate targeted messages at scale using public data, breached information, social media, company context, and industry-specific language.

This does not mean every phishing email is sophisticated. It does mean the ceiling has moved. Messages can be more personalized, more grammatical, more context-aware, and more difficult to distinguish from routine business communication.

Static templates and legacy lexical detection struggle in this environment. If every message can be slightly different, template matching becomes less useful. Defenders must evaluate intent, sender behavior, communication context, and the action being requested.

OAuth, ATO, and Identity Abuse

Some email-related attacks do not depend on malicious email content at all. OAuth consent phishing, session hijacking, MFA fatigue, adversary-in-the-middle phishing, and help-desk social engineering often begin with communication but quickly move into identity systems.

An attacker may convince a user to grant a malicious OAuth application read or send permissions. They may steal a session cookie after MFA completion. They may impersonate an employee to a help desk and trigger an MFA reset. They may gain access to a mailbox and use that account for lateral phishing or supplier fraud.

These attacks show why email security and identity security are increasingly difficult to separate. The email may initiate the event, but the risk quickly moves into Entra ID, Google identity, OAuth grants, session tokens, mailbox rules, software-as-a-service (SaaS) applications, and data repositories.

For buyers, this changes the evaluation criteria. A modern email security platform will be judged not only on whether it blocks messages, but also on whether it helps contain ATO, clean up malicious mailbox rules, revoke OAuth grants, support SOAR and SIEM workflows, and connect suspicious communications to identity activity.

What These Attacks Have in Common

Although these attack patterns differ, they share a common theme. They exploit trust that already exists in the organization.

BEC exploits trust between colleagues. VEC exploits trust between businesses. Trusted-cloud phishing exploits trust in legitimate platforms. OAuth abuse exploits trust in authentication workflows. AI-generated social engineering exploits trust in communication itself.

This does not make content inspection irrelevant. It means content inspection is no longer enough. The modern email security stack needs to combine content analysis, relationship intelligence, identity context, dynamic investigation, response automation, and evidence generation.

How Email Security Evolved

The history of email security is often presented as a sequence of product categories. Secure email gateways gave way to integrated cloud email security platforms, which are now competing with investigation-centric and agentic approaches. Viewed too simply, the market appears to be a series of replacements.

In practice, each generation emerged because attackers changed their behavior and because organizations changed how they communicated. New architectures were not created because previous approaches stopped working entirely. They emerged because existing methods became insufficient on their own.

Understanding this progression helps explain why the market now contains multiple competing models rather than one dominant architecture.

The Perimeter Era

For much of email security’s history, the primary challenge was preventing malicious content from reaching users. Spam campaigns, malware attachments, malicious links, and known-bad infrastructure dominated the threat landscape. Attackers relied on domains and hosting environments that were often disposable and relatively easy to identify.

The resulting architecture was the secure email gateway. Secure email gateways (SEGs) sat in-path through MX redirection, inspected inbound and outbound messages before delivery, and enforced policy at the perimeter. Their capabilities included anti-spam scoring, signature-based malware detection, URL inspection, attachment sandboxing, encryption, outbound DLP, archiving, and continuity.

This model worked because the attack surface matched the control point. If most threats entered through the mail stream and carried identifiable content or infrastructure signals, then inspecting mail before delivery was both practical and effective.

The gateway era also created many capabilities that organizations still depend on. Regulated industries continue to require archive, eDiscovery, retention, encryption at send, continuity, and outbound policy controls. These are not legacy concerns. They remain central to how large enterprises manage communication risk.

The limitation was that the model assumed malicious activity could often be identified through the content or infrastructure associated with the message. As attackers shifted toward impersonation, account compromise, and social engineering, that assumption became less reliable.

The Cloud and Identity Era

The transition to Microsoft 365 and Google Workspace changed email security architecture. Email became one part of a broader cloud productivity environment connected to identity systems, document repositories, collaboration platforms, and SaaS workflows.

This transition created the conditions for integrated cloud email security. ICES platforms are deployed through API integrations rather than MX redirection, allowing them to observe mailbox telemetry, internal communications, identity context, and post-delivery behavior. They could identify, quarantine, claw back, or banner messages after delivery, and they could build tenant-level behavioral baselines based on how people actually communicated.

This was particularly useful for BEC, VEC, reply-chain hijack, ATO, and lateral phishing. These attacks often bypassed traditional inspection because there was little malicious content to inspect. The relevant signal existed in communication history, sender-recipient relationships, identity behavior, and deviations from baseline.

ICES introduced important advantages, but also structural tradeoffs. API-based systems can be faster to deploy and better positioned to observe internal mail, but messages may land before verdicts are rendered. API throttling can constrain scale in large tenants. Outbound DLP and pre-delivery policy enforcement are often thinner than in gateway architectures. Remediation depth varies widely, particularly across shared mailboxes, delegated folders, distribution lists, and lateral campaigns.

The cloud and identity era did not eliminate the gateway. It created a second layer with a different evidence shape.

The Investigation Era

The current phase is being shaped by trusted infrastructure, AI-generated social engineering, multimodal phishing, and operational pressure on security teams. Attackers now use legitimate SaaS platforms, QR codes, image-based content, dynamic redirects, HTML smuggling, and adversary-in-the-middle phishing kits to evade controls that depend on static indicators.

This has shifted the buyer question. Historically, organizations primarily wanted to know whether a message was malicious. Today, they need to know why it was flagged, how the attack works, whether similar messages exist elsewhere, what users interacted with it, and what response action occurred.

That creates demand for evidence packets and investigation workflows. Useful evidence may include reason strings, signal contributions, screenshots, redirect chains, certificate details, domain registration data, URL detonation results, model or rule references, analyst override history, and blast-radius analysis.

This is also where SOAR, SIEM, IT service management (ITSM), extended detection and response (XDR), and managed detection and response integrations become important. Detection that does not produce action is telemetry. The practical value of an email security platform depends on its ability to remediate across mailboxes, revoke malicious OAuth grants, remove mailbox rules, cluster campaigns, contain ATO, and reduce security operations center (SOC) workload.

Why Multiple Architectures Continue to Exist

Email security rarely evolves through clean replacement. Organizations operate under different constraints, and those constraints shape procurement.

A financial institution may require SEG-grade archive, encryption, continuity, and outbound DLP. A cloud-native technology company may prioritize ICES because its primary problem is BEC and supplier fraud. A data-sensitive enterprise may care about connecting phishing to DSPM, DLP, and managed detection, defense, and response (MDDR) workflows. A resource-constrained security team may prioritize agentic investigation and autonomous remediation.

These priorities are not mutually exclusive. Many mature programs will continue to run layered architectures that combine MX-in-path controls, API-based ICES, platform-native controls, identity integrations, browser protections, and response automation.

The next phase of the market is therefore unlikely to produce a single winner. It will be shaped by how effectively each architecture contributes useful context, evidence, and action.

Modern Email Security Architectures

The evolution of email security has produced a market that is more diverse than at any point in its history. Organizations face a wider range of threats than they did a decade ago, while security teams operate under different regulatory obligations, staffing constraints, risk tolerances, and operational priorities.

As a result, the market no longer revolves around a single architectural model. Instead, several approaches have emerged, each built around different assumptions about how attacks occur and what security teams need most.

Policy and Perimeter-Centric Architectures

Policy and perimeter-centric architectures are typically associated with secure email gateways. They inspect messages before delivery, enforce inbound and outbound policy, and provide capabilities such as malware analysis, URL inspection, attachment sandboxing, encryption, archive, eDiscovery, continuity, and outbound DLP.

Their strength is breadth. Many organizations require email security to support governance and compliance alongside threat detection. For these buyers, SEG capabilities remain essential.

The gap is context. Perimeter-centric architectures can struggle when attacks contain few traditional indicators of compromise, especially BEC, VEC, internal-to-internal lateral phishing, and account takeover. In those cases, identity context and communication history may matter more than attachment or URL inspection.

Relationship and Identity-Centric Architectures

Relationship and identity-centric architectures emerged because many modern attacks exploit existing relationships rather than suspicious infrastructure. These platforms use API deployment, mailbox telemetry, communication history, tenant baselines, sender-recipient patterns, authentication activity, and user context to identify suspicious behavior.

This model is particularly strong against BEC, VEC, executive impersonation, account takeover, and reply-chain hijack. The relevant signal often exists outside the message itself.

The tradeoff is that attackers combine behavioral deception with infrastructure abuse, trusted-cloud hosting, image-based phishing, quishing, and HTML smuggling. Relationship intelligence remains important, but it may not provide a complete view of attacks where the primary signal exists in the destination workflow rather than the communication relationship.

Investigation and Evidence-Centric Architectures

Investigation and evidence-centric architectures focus on understanding how attacks operate. They dynamically inspect URLs, follow redirect chains, capture screenshots, detonate content, analyze infrastructure, evaluate domains, and produce artifacts that analysts can review.

This approach is especially relevant for trusted-cloud phishing, QR-code attacks, adversary-in-the-middle kits, dynamic redirects, and attacks that hide malicious behavior behind legitimate services.

Its strength is explainability. Security teams, auditors, insurers, and executives need to understand why a verdict was reached. The challenge is operational balance. Deep investigation must be fast enough, scalable enough, and easy enough to integrate into SOC workflows.

Agentic and Automation-Centric Architectures

Agentic and automation-centric architectures focus on operational scale. They use LLMs, small language models, specialized agents, and automated workflows to investigate messages, evaluate intent, assemble evidence, cluster campaigns, trigger remediation, and reduce analyst workload.

This model reflects a practical problem. As AI lowers the cost of creating attacks, defenders need to lower the cost of investigation and response. Agentic systems attempt to function as force multipliers for SOC teams.

The open question is governance. Organizations need to understand how much authority they are willing to delegate to automated systems, especially when those systems can quarantine messages, trigger password resets, escalate incidents, or initiate XDR and SOAR workflows.

Why No Single Architecture Will Win

The existence of multiple architectures does not mean the market has failed to converge. It reflects the fact that organizations are solving different problems.

A regulated enterprise may prioritize SEG controls. A cloud-native organization may prioritize ICES. A security team facing trusted-cloud abuse may prioritize investigation and evidence. A lean SOC may prioritize agentic automation.

Most mature organizations will combine elements of several models. The procurement question is not which architecture is best in the abstract; it is which combination produces the most useful prevention, context, evidence, and response for the buyer’s specific environment.

Market Outlook: Email Security Beyond the Inbox

The email security market has spent three decades expanding the scope of what organizations expect from the category. The first generation focused on preventing malicious content from reaching the inbox. The second focused on understanding the relationships and behaviors surrounding communications. The current generation emphasizes investigation, response, and operational efficiency alongside detection.

The next phase is unlikely to be defined by one breakthrough technology. It will be shaped by several trends already visible in buyer priorities, attack patterns, and vendor roadmaps.

Identity and Email Continue to Converge

Email security and identity security are becoming more interconnected. BEC often relies on account compromise. OAuth abuse often begins with social engineering. Session theft, credential harvesting, MFA fatigue, and help-desk manipulation frequently involve communication channels alongside identity systems.

This does not mean email security vendors become identity vendors. It means email products need identity context, and identity products benefit from communication context.

Buyers should expect tighter integration with Entra ID, Google Identity, OAuth telemetry, session activity, ATO detection, mailbox-rule cleanup, and identity threat detection and response workflows.

Detection efficacy remains important, but detection claims are becoming harder to differentiate. Nearly every major vendor claims strong performance. Buyers want to know whether a platform can explain its decisions.

Evidence packets, detection-as-code, model cards, signal contributions, reason strings, analyst override paths, and exportable case records will become more important during procurement. Opaque classifiers may still perform well, but buyers under pressure from boards, insurers, auditors, and regulators prefer systems that can produce defensible evidence.

Response Becomes More Important Than Detection

Security teams evaluate email security based on operational outcomes. The relevant questions are no longer limited to whether a vendor detects threats. Buyers want to know how quickly messages are removed, whether the system can claw back across shared mailboxes and delegated folders, how it handles OAuth grants and mailbox rules, whether it clusters related campaigns, and how well it integrates with SOAR, SIEM, ITSM, XDR, and MDDR workflows.

Mean time to remediation, blast radius reduced, SOC hours saved, ATO containment time, and false-positive reduction are becoming more useful metrics than raw detection rates.

Communication Security Expands Beyond Email

Email remains critical, but attacks now move across Teams, Slack, SharePoint, Google Drive, WhatsApp, LinkedIn, Discord, SMS, supplier portals, and browser-based workflows. Buyers should distinguish between integrations that merely forward alerts and integrations that provide message-level enforcement such as quarantine, removal, revoke, or user protection.

The future of communication security is less about protecting a single channel and more about following social engineering across the channels where work actually happens.

Email and data security are also moving closer together. A phishing email may lead to credential theft, credential theft may lead to unauthorized access, and unauthorized access may lead to data exposure. The incident spans email, identity, SaaS, and sensitive data, even if it begins with one message.

This creates a closer relationship between email security, DSPM, DLP, insider risk, and MDDR. Buyers should expect more vendors to position phishing as the beginning of a broader attack lifecycle rather than a standalone category.

CISO Implications

The email security market is becoming more complex, not less.

For most organizations, the future of email security will not involve replacing one architecture with another. Instead, it will involve assembling a layered security program that combines prevention, contextual analysis, investigation, and response capabilities.

As a result, the primary challenge for CISOs is no longer selecting a single email-security platform. It is determining which capabilities most directly reduce risk within their organization’s operating model, staffing constraints, regulatory requirements, and threat environment.

The most effective email-security programs over the next several years will not necessarily be those with the highest detection rates. They will be the programs that most effectively align technology investments with business risk, operational realities, and incident-response requirements.

Align Architecture to Risk

Different email-security architectures solve different problems.

Organizations operating in highly regulated industries continue to require secure email gateway capabilities such as encryption, archiving, continuity, eDiscovery, and outbound data loss prevention. These requirements do not disappear simply because phishing techniques evolve.

Organizations most concerned with business email compromise, supplier fraud, account takeover, executive impersonation, and lateral phishing may place greater emphasis on relationship- and identity-centric platforms capable of understanding communication context and behavioral anomalies.

Organizations facing sophisticated phishing campaigns that leverage trusted infrastructure, QR codes, image-based deception, adversary-in-the-middle workflows, or complex redirect chains may benefit from investigation-centric approaches that provide greater visibility into how attacks operate and why they were detected.

Organizations struggling with analyst workload, staffing shortages, or increasing alert volumes may place greater value on automation, orchestration, and agentic investigation capabilities designed to accelerate response and reduce manual effort.

The objective should not be identifying the “best” architecture. The objective should be identifying which combination of capabilities best supports the organization’s security operating model.

Indicators That Additional Investigation Capabilities May Be Needed

CISOs should consider strengthening investigation-centric capabilities when they observe one or more of the following conditions:

• Trusted-cloud phishing consistently bypasses existing controls.

• Analysts spend significant time manually validating phishing verdicts.

• Security teams struggle to explain detection decisions to executives, auditors, or insurers.

• QR-code phishing, browser-based attacks, or credential-harvesting campaigns are increasing.

• Security operations teams lack sufficient visibility into post-click attack behavior.

• Incident investigations frequently require evidence gathering from multiple tools.

These conditions often indicate that detection alone is not providing sufficient operational context.

Evaluate Operational Outcomes, Not Feature Lists

Many procurement processes still focus heavily on feature comparisons and detection claims. Those factors remain important, but they rarely determine long-term success. The more useful question is whether a platform improves operational outcomes.

Examples include:

• Reduction in business email compromise exposure.

• Faster remediation of malicious messages.

• Lower analyst investigation time.

• Improved visibility into account compromise activity.

• Reduced incident-response effort.

• Better integration with existing security workflows.

• More defensible evidence for audits, insurance reviews, and executive reporting.