The Great DLP Reset: Securing Data in the Age of SaaS, Cloud, and AI

DLP is being rebuilt for new runtimes in SaaS, cloud data, AI and agentic workflows.

Executive Summary

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Data Loss Prevention (DLP) is undergoing a structural reset.

What was once a fragmented set of point controls anchored to endpoints, networks, and email gateways is being rebuilt into a unified, discovery-led data control plane designed for a world where data no longer sits still, users no longer operate within defined perimeters, and AI systems now participate in how data is created, transformed, and exfiltrated.

The legacy DLP model is breaking, not because detection has failed, but because the underlying assumptions no longer hold. Traditional DLP relied on stable data patterns, centralized enforcement points, and human-driven tuning. Modern environments defined by SaaS sprawl, cloud data gravity, and GenAI workflows have invalidated each of these assumptions.

Our report argues that the next era of DLP will not be defined by better pattern matching or broader coverage, but by a fundamental architectural shift.

1. Our thesis for this research is that Data Loss Prevention (DLP) is being rebuilt into a discovery-led control plane for modern runtimes (SaaS, cloud data, and AI/agent workflows). Traditional Enterprise DLP, Integrated DLP, and Cloud-Native DLP categories are an insufficient way of delineating DLP at the program level. A DLP reset is needed. In the DLP reset, the winners are not the platforms that generate the most alerts, but those that reduce operational burden while delivering measurable risk reduction through automated remediation, real-time prevention where feasible, and audit-grade evidence (and lineage where possible).

2. DLP is being rebuilt into a discovery-led control plane: This includes continuous classification, which becomes the truth layer where identity/entitlements provide decision context, and enforcement shifts from a few chokepoints to a set of distributed enforcement planes (SaaS APIs, inline SSE/SASE/GW, browser/session, endpoint, and AI prompt/agent surfaces).

3. The Winners: The winners won’t be the platforms that generate the most alerts. Rather, they’ll be the ones that measurably reduce risk, improve visibility and control of data with AI and agents and offer lower operational burden, automated remediation, and audit-grade evidence.

Key Insights On How DLP Is Changing

DLP is undergoing a reset and moving from deterministic, enforcement-point-centric controls to a discovery-led data control plane model that combines continuous classification, identity, context, data labeling, and automated remediation across SaaS, cloud data, endpoints, browsers, user driven and agentic AI workflows. AI adoption has reached 73% of enterprises in 2026, while real time security governance is just beginning to emerge at 7%. Browsers are emerging as a key defense mechanism for AI and Data loss as users spend ~75% of their work day either working in a web browser or attending virtual meetings.

Based on discussions with SACR clients, briefings by vendors, emerging vendors, and public information:

• The center of gravity is shifting toward faster time-to-first-signal (TTFS), faster time to prevention and much more focused on lower tuning burden to avoid false positives, and improve classification.

• API, SaaS-first and DSPM-led control planes generally offer lower burden, while classic endpoints and network suites remain high-burden even when their breadth is strong.

• Inline SSE and SASE DLP (e.g., Prisma Access, Netskope, Zscaler, etc) remains a high-value path for broad enforcement, but it carries structural burden, for example requiring traffic steering or custom reverse proxying, policy pattern and profile management, and dependency on traffic flowing through enforcement points.

• M365-native DLP (for example Microsoft Purview) can deliver high value quickly inside Microsoft environments, but the operational tax includes activities such as endpoint onboarding, policy complexity, and false positive or custom data classification tuning which remains non-trivial.

• Expanding Agentic Platforms and AI interaction and enforcement points is complicating the future of DLP especially for emerging agentic platforms and their workflows.

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Actionable Summary

• Establish a truth layer for the data security program (discovery/classification): If you cannot answer where sensitive data is and who can access it, enforcement will be noisy and politically hard to sustain and preventive remediation efforts will not be easily achievable.

• Add in essential context (identity, entitlements, sharing posture): Combine a truth layer (DSPM and discovery) with context (identity and entitlements) to inform enforcement and direct remediation efforts or automations (for example data owner guidance or automated approvals).

• Choose enforcement points intentionally (SaaS API first where possible; inline and endpoints where justified): Utilize a targeted enforcement point layer (for example, by using SaaS APIs, Inline SSE/SASE/GW where needed, and focus on endpoints only where necessary - e.g. users leveraging traditional protocols and data sharing infrastructures like SMB/SAMBA Drive Shares or sync-share file solutions).

• Operationalize GenAI runtime controls, ideally using browser controls (prompt/session + agent tool-call logging): Define controls for users for example prompt pasting, Copilot access scope, and agent tool-call auditing and logging and don’t assume classic DLP policies will translate to the new emerging areas.

• Measure data loss prevention outcomes (automation, risk reduction and adequate evidence): The reset winners are those that can revoke sharing, redact, delete or even mask and encrypt content in SaaS, and produce evidence trails with less analyst labour and without increasing burden or creating a ticket factory outcome for your human participants.

• Focus on Audit-grade evidence and end-to-end data lineage (what counts): The key components are the event logs, the actors involved, the object impacted, the action taken, the precise timestamp, data lineage trace and proof of the remediation if applied.

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Introduction, Market and Industry Context

The Great DLP Reset

The Great DLP Reset signifies a major transformation in architecture, detailing several essential shifts in how data security initiatives must evaluate DLP tools. This movement is steering the development of new vendor strategies to resolve longstanding challenges within data loss prevention programs. DLP is transitioning from conventional, static perimeter security toward a discovery-driven data control plane, tailored for the rapid pace of Cloud, SaaS, AI, and autonomous agent interactions.

Market Definition: Modern DLP

Modern DLP (as used in this report) is defined as solutions that, when combined, deliver a set of capabilities that:

Data Loss Prevention (DLP) is a security strategy and set of technologies designed to detect, monitor, and safeguard sensitive information from unauthorized access, accidental exposure, or malicious exfiltration. It governs sensitive data at rest, in motion, and in use across endpoints, networks, and cloud services. This definition intentionally expands beyond content inspection at fixed enforcement points and reflects the market’s convergence with DSPM, SaaS security and governance, browser security, and AI governance.

Definitional Technology, Feature(s), and Service Lines

DLP tools perform automated data discovery and classification with deep content inspection, and contextual policy enforcement (block, quarantine, encrypt) focused on the enforcement of real-time data prevention on real-time user interactions. Integrations commonly include CASB/SSE/SASE, network gateways and endpoint agents to govern data flows. Emerging patterns include browser extensions and agentic platforms with limited data control and visibility. Modern DLP tools prevent sensitive data exfiltration across endpoints, SaaS, web/email, and cloud via classification and policy enforcement.

Exclusion criteria: Vendors that deliver products and services of AI prompt inspection and API based application proxies that focus primarily on prompt threat inspection and context (these solutions are more focused on agentic and workflows: See Unified Agentic Defence Platforms (UADP).

What It Is This Reset In DLP?

The architecture of the DLP reset defines a new truth layer combined with context and distributed, establishes continuous discovery and classification to determine what the data is and where it is, and reinforcement that includes remediation and substantive evidence.

• Truth Layer: Edes across premises, SaaS, cloud, AI services and endpoints.

• Context: Integrates identity, entitlements, sharing posture, and behavioral analytics to provide decision intelligence and reduce false positives.

• Distributed Enforcement: Shifts control from fixed chokepoints to multiple planes including SaaS APIs, inline SSE/SASE/GW, browser sessions, and AI prompt surfaces.

• Remediation: Emphasizes automated actions such as revoking links, redacting sensitive data fragments, engaging data owners through autonomous workflows and chat applications (Slack/Teams) and can perform quarantining of assets at machine speed.

• Evidence: Produces audit-grade logs and data lineage traces that provide proof of remediation and a forensic chain of custody for investigations in the case of an intentional data exfiltration by a user.

What it Isn’t

Rather than a traditional solution defined by pervasive regex, the DLP reset moves away from models that necessitate constant manual adjustment and result in a ticket factory style SOC environment. Such legacy approaches impose a heavy operational burden on personnel at every stage, from policy refinement and alert triage to the execution of enforcement actions.

• Regex Everywhere: Traditional DLP relies on deterministic pattern matching and static signatures (e.g., RegEx for Social Security Numbers) which fail to understand content depth or context in modern, unstructured data flows.

• Manual Tuning: Legacy systems require constant human intervention to manage exception sprawl and adjust rules for stable data schemas that no longer exist in ephemeral cloud workloads.

• Ticket Factory: Without identity or behavioral context, classic tools generate massive volumes of false positives, turning security operations into ticket factories focused on bulk-closing alerts rather than active risk mitigation.

The DLP Control Plane (Discovery and labelling-led)

Modern DLP is best understood as a control-plane model which includes decisioning and orchestration that drives actions across multiple enforcement planes. It breaks the regex everywhere plus manual tuning era and ticket factory pattern by separating the intelligence plane that determines what matters, from the enforcement planes that execute data security control. Put simply, data discovery, a core aspect of DSPM technology, DSPM is great at discovery and classification of data, determining location and various use cases, the DSPM tools and the data they discover and classify helps inform enforcement points, placement of key inspection and control layer functions across an enterprise, its users and any AI agents it is operating. It can be used to fine tune data loss programs for actual data loss prevention, vs only being used like they are in most DLP programs, as data monitoring solutions, or focused on limited data classifications such as PII or well known structured data types.

The Intelligence Plane (truth, context and orchestration)

Modern DLP Control plane architecture is fundamentally sub-divided into two major components: the Intelligence Plane and the Enforcement Plane. This structural division acts as the core machinery of the reset, effectively separating the centralized intelligence required to determine data significance from the distributed planes required to execute precise controls.

• Truth layer (continuous discovery and classification): Establishes what the data is, where it lives, and how it is moving. Where vendors offering Data Security Posture Management (DSPM), handle data discovery across premise and cloud, properly classify and label, and synchronize data sensitivity labeling across Microsoft (Purview/MIP) and Google Workspaces. These labels serve as critical truth layer context for the enforcement planes.

• Context layer (identity, entitlements, sharing posture and behavior): Determine who can access it, how it’s exposed, and what the risk scenario is to apply the best controls to match the scenario.

• Decisioning and prioritization: Reduces noise by ranking what is materially risky (not merely what matches a simple pattern), augmented with AI and LLM content analysis and labeling.

• Automation and orchestration: Translates policy intent into repeatable orchestrated actions (for example warn, block, redact, revoke sharing, quarantine, label, encrypt) ideally with guardrails, contextual policy control and rollback capabilities.

• Evidence and auditability: Produces investigation-ready data lineage and timelines with actor, object, action, timestamp, and remediation proof (plus end to end data lineage where possible).

The Enforcement Plane (aka distributed control points)

The Enforcement Plane represents the distributed control points where security policies are actively applied to data across various environments. These planes are responsible for executing specific actions, such as blocking, redacting, or quarantining, at the precise moment a violation occurs, moving beyond simple detection to active data risk mitigation. By shifting enforcement from a few centralized chokepoints to distributed surfaces like SaaS services, APIs, inline SSE/SASE/GW, and the enterprise browser, organizations can achieve more precise control over modern data movement and usage patterns. SACR believes that enterprises must look at their data security architecture through this lens to achieve better data loss prevention outcomes.

The Enforcement Plane includes:

• SaaS/API enforcement: Out-of-band controls and remediation inside collaboration and business apps and in AI workflows and agentic platforms.

• Inline SSE/SASE/GW enforcement: Real-time inspection/control for web and SaaS traffic where steering and SSL/TLS decryption is justified.

• Browser and session enforcement (last-mile runtime): In-session controls over the dominant leakage verbs (copy/paste, upload/download, printing, screen capture), including browser based GenAI interactions.

• Endpoint enforcement: Device-level controls for local exfil paths (USB, local copies, unmanaged sync, print, clipboard, screen capture), especially for regulated or high-risk endpoints and end user workspaces.

Emerging Problems in Data Security and Data Loss Prevention

Classic DLP approaches struggle because they assume clear architectural or isolated choke-points, stable data schemas and patterns, and that there are human resources available for manageable tuning and remediation. Modern environments violate those assumptions dramatically, leading to failed deployments and fragmented enforcement with no clear unification of policy.

Organizations are facing a modern data exposure problem characterized by:

• SaaS sprawl and collaboration-first workflows for example sensitive data moves through Slack, Microsoft Teams, Google Drive, One Drive, Microsoft GitHub or various SaaS applications like Salesforce, marketplaces and similar SaaS surfaces.

• Cloud data gravity has shifted towards sensitive data in warehouses, data lakes and object stores with complex access paths and various data sharing integrations (even between SaaS applications).

• User controlled GenAI chat applications and agentic workflows, prompt-based exposures, copilots with broad reach and data moving data between data stores, file systems, applications or other AI agents and users.

• Expanding SaaS-based agentic platform services and no-code providers (Salesforce Agents, Claude Cowork, OpenClaw hosting, Eigent(open source), Zapier, Airtable etc)

Modern DLP Reset Storyline: Market Phases

Why Classic DLP Became High-Burden

Classic Data Loss Prevention (DLP) approaches have become a high-burden due to several core problems that fundamentally require a reset. These issues include a significant operational tax from noise and tuning burden caused by high false-positive rates and exception and enforcement gap sprawl. Classic DLP suffers from weak visibility into where sensitive data exists and which users have the entitlement(s) to reach it. Often various DLP tools were deployed in silo’s and not properly configured to unify enforcement or consistent policies and utilized various editions of data labeling and classification techniques. Architecturally, it has a poor fit for modern environments characterized by SaaS-native sharing and API-driven data movement that bypass intermediary choke points. Classic DLP is proving to be even a weaker fit for AI usage, struggling to govern probabilistic AI workflows, prompt-based exposures, and agentic tool calls.

While these foundational mechanics still have a place for performance-heavy compliance tasks, they are insufficient for modern runtimes and emerging architectures which are requiring greater granularity and data-in-use understanding. This legacy framework was effective only as long as the enterprise controlled the infrastructure, the network, application deployment architectures and the devices. However, the shift toward Software-as-a-Service (SaaS), Cloud-native infrastructures, and now Artificial Intelligence (AI) has fundamentally disrupted this paradigm.

The Core Problems and Operational Tax

• Noise and Tuning Burden: High false-positive rates and exception sprawl leading to DLP-as-a-ticket-factory style outcomes, overwhelming data security teams and causing alert fatigue for security operations.

• Weak Visibility and Graph Visualizations for Analysis: Traditional tools lacked insight into what sensitive data exists at rest and who has the entitlement to reach it across fragmented environments. They lacked significant visualizations and graph databases to properly articulate vast interconnected data flows between various entities and connect it with data in-use visualizations.

• Architectural Fragility: Dependence on network interception makes steering traffic and rearchitecting for DLP fragile and politically difficult as data moved directly between SaaS apps via APIs (for example N8N and Zapier style no-code and workflow automation tooling).

• Weak AI Fit: Static rules cannot govern emerging probabilistic AI workflows, prompt-based exposures, or agentic tool calls and need additional enhancements to deliver that functionality through integrated APIs.

Market Shift Timeline: Data Loss Prevention Challenges 2000s-Present

Late 2000s to early 2010s: DLP becomes a Mainstream Enterprise Control

• High policy authoring and tuning burden (regex, Exact data matching/hashing (EDM), fingerprinting) and large operational overhead

• High false positives without strong context, leading to alert fatigue and DLP-as-a-ticket-factory

• Cultural and adoption friction: DLP often perceived as blocking business productivity without clear outcomes

Mid 2010s: Cloud migration begins to erode chokepoints

• Architectural dependency on interception of network traffic, steering became fragile and politically difficult.

• Visibility gaps for data-at-rest across hybrid environments and in SaaS and cloud storage (not just data-in-motion).

• Identity use and entitlements started to dominate outcomes, but classic DLP has weak native entitlement context and is mostly limited in scope to traditional endpoint or network traffic choke-points, file and data storage systems and sharing protocols.

Late 2010s to early 2020s: SaaS sprawl and collaboration-first work explode the policy burden

• Proliferation of enforcement surfaces (too many places to write and maintain consistent policies)

• Weak evidence and investigation context (who shared what, with whom, and why)

• Remediation becomes workflow-heavy (revoking shares, cleaning repos, fixing permissions) and doesn’t scale well via manual tickets

Early to mid 2020s: DSPM and discovery-led approaches reshape expectations

• Classification Accuracy limitations: Classification accuracy and explainability at scale (trust becomes the gating factor) for adoption to be viable.

• Shadow SaaS (also called Shadow IT) and SaaS data Sprawl: Connector and data sprawl and integration reality (coverage of data in storage and motion becomes dependent on APIs, audit logs, and action depth) often requiring knowledge of SaaS adoption to integrate DLP functions.

• Control-plane complexity: Orchestrating consistent actions across heterogeneous tools without breaking business workflows meant that end to end visibility and discovery challenges became rampant.

• Centers of gravity emerge for data classification labeling: Microsoft Information Protection/Purview, Google Workspace become centers of gravity for key data labeling hierarchies, enabling greater federation of enforcement across the enforcement layers

2023 to present: GenAI and agentic workflows create a new DLP runtime

• Governance: Governance of non-file data flows (prompts, outputs, tool calls) where content inspection at gateways is insufficient.

• New UX and policy questions: Warn user vs block or data redaction require deep user and use case understanding, and what constitutes sensitive in a prompt context is required for proper enforcement and policies.

• Evidence and audit requirements: Logging agent actions and maintaining chain-of-custody for investigations and having a forensically sound chain of custody and data lineage become a new requirement.

• Model and AI agent uncertainty: Controlling stochastic systems and verifying efficacy claims in production become very challenging and introduce uncertainty.

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The Great Reset: Why DLP is Back

We think DLP programs need to reframe their deployment architectures around our new framework. Today, DLP deployments span a variety of enforcement plane capabilities and fragmented abilities to control data (also called actions), some using older or more basic methods of data identification, classification and enforcement. Meanwhile, more modern classification tooling such as that of machine learning, semantic classifiers and the broader variety of data security actions can be embedded across enforcement points in a more unified manner. The old frameworks were moderately effective because the enterprise controlled the infrastructure, the network, the communication channels, and the computing devices that made data residency and transit patterns static and manageable.

Though the basics of traditional DLP still have a place in modern DLP systems (for example using Exact data match or regular expressions, for delivering higher performance or speed), they are not the capabilities that offer the best detection accuracy, classification depth and deep semantic understanding of content and context that is needed for future data loss prevention, especially agentics. Today’s disparately deployed DLP solutions also need a better grounding in truth, a new defining truth-layer to align various enforcement points and their policies. Modern DLP implementations are plagued by severe architectural instability and have various nuanced limitations. Security departments are frequently trapped in a cycle of managing inconsistent, siloed controls that span across endpoints, network gateways, collaboration platforms, and browser environments. This disconnected approach necessitates a comprehensive structural overhaul, shifting toward a unified control plane anchored by a discovery-driven data strategy. Failure to implement this transition prevents organizations from maintaining uniform policy enforcement across the decentralized points needed to regulate real-time data interactions by modern users and emerging AI agents.

Emergence of the Ticket Factory Overload

In traditional DLP solutions, this phenomenon is usually referred to as alert fatigue, or false positive overload, or as the swivel-chair security problem - where practitioners need to stitch various events and data security contexts across multiple silo’s of tools. But generally, this is why traditional DLP tends to turn security teams into ticket factories, and in the new world of DLP the core idea is to avoid it. Traditional DLP solutions rely on static rules, regular expressions (regex), and exact data matching (EDM). For example, if a user tries to move a file under the old classification schemes where it looks like it has 16 digits, the regex based DLP flags it as a credit card number, even if it’s not, simply because the data is formatted similarly. This is predominately because these older tools lacked context and depth of understanding of semantics and consideration of other data content context, they often generate massive volumes of false positives. For example, a 2024 Security Boulevard SOC Efficiency Study noted that nearly one-third of all security alerts are false positives, and legacy DLP classification approaches were a primary offender.

The Old Paradigm Created a Tremendous Toll on the SOC

In the old ticket factory paradigm, analysts with DLP tooling and enforcement points without solid context often spend their entire day bulk-closing tickets just to keep their heads above water, investigating business-as-usual activities rather than actual data exfiltration events. The SOC or data security teams become a ticket factory focused on closing IT service desk requests rather than hunting threats. As data classification, context and semantic understanding has increased, so too has accuracy of alerting, and a great reduction in false positives.

Challenges in Modern Day Environments

• SaaS Sprawl: Sensitive data now moves from both managed and unmanaged endpoints, through communications tools like Slack, Microsoft Teams, and GitHub and are also often outside the view of traditional DLP style gateways and choke points.

• Unified Data Labeling Strategy and Synchronizing Across the stack: Google data labeling or Microsoft Purview, tend to be gold standards. Using modern DSPM tools like Cyera, Palo Alto Networks, Varonis and others, they can properly translate labeling as files or data move between different environments.

• Cloud Data Gravity: The emergence of Data lakes and object stores has created new complex access paths that violate the assumptions of legacy choke-point models.

• The AI runtime increasingly routes through the browser: Most SaaS work and many GenAI interactions happen in-browser, which makes browser and session control a first-class last-mile enforcement-plane. Endpoint and network DLP are not obsolete, but they are often insufficient alone for browser-mediated leakage (copy/paste, upload/download, printing, screen capture) and for prompt and agent workflows.

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Trends driving the change

These shifts are necessary to address modern environmental challenges, including SaaS Sprawl, where sensitive data moves through tools like Slack and GitHub outside traditional gateways, and Cloud Data Gravity, which involves complex access paths in data lakes and object stores and labeling problems. As the AI runtime increasingly routes through the browser, traditional endpoint and network DLP are often insufficient to control browser-mediated user actions like copy/paste and AI prompt interactions. It’s notable that not all enforcement points have orchestrated data control, nor do they all share classification and labeling, an industry standardization problem that some vendors are addressing. To address this, vendors focused on data loss have been centralizing policy enforcement based on document and file labeling within data classification functions (for example, labels provided by Microsoft Purview or Google Workspace) in the enforcement layer, most providers now offer label based enforcement, helping to unify enforcement actions across the enforcement layer. To address the data relabeling issue, some enforcement providers offer relabeling capability for documents that pass through their inspection points and help with freshness of that unified enforcement. Some vendors in the great reset DLP enforcement layer are also leveraging the Microsoft Security graph for risk signals to enhance threat prevention context coupled with visibility and control of user activities with sensitive data type policies for example conditional access signals like geolocation, endpoint, fingerprints, historical risk scoring,etc.

Why the browser has become a new enforcement plane

The shift toward web-based user workspaces and the rise of AI chat interfaces have transformed the browser into a vital enforcement plane for data security. As organizations move almost entirely to SaaS applications, new browser-based solutions and extensions are emerging as essential complementary tools to address visibility gaps. While GenAI chat is driving the initial demand for integrated Data Loss Prevention (DLP), the market is rapidly expanding toward agentic platforms that require sophisticated API-integrated services for comprehensive data control. Standard network and endpoint security often fail to capture granular, in-session activities. Consequently, the browser, combined with integrated APIs for agentic services, provides critical last-mile governance over how users and AI agents manipulate sensitive information. Modern browser-oriented security now leverages capabilities like DOM inspection, fingerprinting, and WebAssembly containers to function as a primary enforcement mechanism, offering robust detection, classification, and control at the edge.

Benefits include:

• SaaS work that happens in-browser: In today’s modern SaaS applications, the most common collaboration and administrative actions occur in web apps.

• GenAI interactions that happen in-browser: Chat oriented prompts, copilots, and embedded assistants often run in a browser session even when backed by enterprise models.

• Copy/paste/upload/download and application context dominate leakage methods in browsers: This means that last-mile user actions are frequently the decisive exfil path, and they can bypass network or API-only controls without browser and session level enforcement. This capability is achieved either through browser extensions, injections of javascript to monitor dom-tree and execution elements, or enterprise browser replacements.

The DLP Technology and Deployment Evolution

Modern DLP Outcome Imperatives

1. Establish a Discovery-led truth layer by shifting from Regex to AI Classification, Context Enrichment, and Semantic Understanding to classify data based on actual sensitivity and user intent, eliminating the need for thousands of static regex rules.

2. Implement Automated Triage and Streamlined Incident Response by utilizing Agentic AI to autonomously triage alerts, auto-close false positives, and only escalate validated threats, preventing the SOC from becoming a data loss ticket factory.

3. Empower the End-User with Real-Time Just-in-Time Coaching by leveraging automated workflows and browser-based alerts via platforms like Slack or Microsoft Teams to prompt users for justification or self-correction during policy violations, effectively decentralizing enforcement and reducing low-risk tickets.

4. Drive Proactive Remediation and Prevention through Agentic Campaigns where modern DSPM and DLP solutions evolve toward autonomous, real-time remediation across the truth layer and prevention at the enforcement layer, enabling enterprises to launch agentic communication campaigns or continuous evaluation for prevention and engagement with data owners to address potential risks preemptively.

5. Deploy Distributed Automated Enforcement with machine-speed interventions, including real-time blocking, contextualized DSPM, runtime encryption, and asset quarantine, by deploying uniform protections across endpoints and cloud gateways to effectively halt exfiltration and secure intellectual property.

When Selecting Data Loss Prevention Controls, Consider the Data Loss Prevention Trade-Off

This infographic illustrates The Data Loss Prevention Value Trade-Off using a four-quadrant matrix that evaluates cybersecurity strategies based on their Value versus their Operational Burden. The chart highlights an Optimal Zone in the upper-left quadrant, where modern solutions like DSPM-DLP convergence, AI-era controls for prompts, and automated remediation and prevention workflows provide high security value with relatively low maintenance effort.

In stark contrast, the Danger Zone in the lower-right quadrant contains legacy methods, such as classic network controls and deterministic pattern matching, which are depicted as having low value and high operational complexity. Ultimately, the visual serves as a strategic roadmap, encouraging organizations to shift away from labor-intensive, rule-based systems toward adaptive, SaaS-first enforcement and automated workflows to maximize efficiency and protection.

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Market Evolution in DLP

New Building Block Layers Emerge

Modern DLP stacks are increasingly assembled from interoperable component layers rather than a single monolith. This modular approach allows enterprises to move beyond legacy perimeter-based security toward a discovery-led data control plane. The landscape of Data Loss Prevention (DLP) is evolving from static, rule-based systems to highly intelligent, context-aware and AI/agentic enabled platforms designed to secure modern, AI-driven workspaces. Emerging features in this space focus on understanding intent, tracking data throughout its lifecycle, and governing both human and machine identities.

Here is a look at the emerging concepts and capabilities in modern DLP:

Autonomous AI Investigation

Instead of relying on rigid keyword matching or regular expressions that generate massive alert fatigue, modern DLP platforms deploy AI to autonomously investigate potential data loss events. These intelligent systems analyze incidents across multiple dimensions—such as the data itself, the systems involved, human behavior, and the surrounding business processes. By interpreting the context and intent behind a data transaction, the system can distinguish between legitimate business workflows and genuine risks, effectively automating alert triage and response.

Governance of AI Agents and MCPs

As enterprises increasingly deploy autonomous AI agents to execute tasks, DLP must expand to secure these non-human actors. Emerging platforms provide continuous discovery and governance by enforcing action policies on autonomous behavior. This includes inline inspection of interactions, such as monitoring Model Context Protocol (MCP) calls, to prevent prompt injections, jailbreaks, and unintended data leakage across the AI supply chain. See Agentic Platform examples below.

Graph-Based Data Lineage

Rather than inspecting files in isolation, modern DLP tracks the entire lifecycle and provenance of data. By capturing a continuous record of where data originated (e.g., a secured internal database), how it has been modified, and who has interacted with it, systems can accurately assess risk. For instance, if sensitive data is copied, reformatted, and pasted into an unauthorized generative AI prompt, the DLP system recognizes the data’s sensitive origin and enforces protection policies, drastically reducing false positives.

In-Line Browser Guardrails

With the web browser becoming the primary interface for SaaS and Generative AI applications, emerging DLP solutions apply granular, last-mile controls directly within web sessions. Instead of simply blocking entire websites, these capabilities monitor text inputs, drag-and-drop actions, and file uploads in real time. They can dynamically disable copy/paste functions for specific fields or automatically redact sensitive information before it is submitted to public AI models, allowing organizations to adopt AI productivity tools safely.

Dynamic, Risk-Adaptive Controls

Modern DLP is moving away from static allow or block rules toward dynamic enforcement based on continuous behavioral and intent analysis. By calculating real-time risk scores based on user activity, the system can baseline normal behavior and automatically adjust its posture. If an employee exhibits anomalous or high-risk behavior, such as a sudden spike in downloads or accessing unusual repositories, the DLP controls automatically tighten to intervene. Once the behavior returns to normal, the restrictions are relaxed, minimizing friction for legitimate work.

Emerging Agentic Platforms in 2026

*Consideration List - not exhaustive

Defining the Unified Data Loss Control Plane (DLCP) for the Modern Enterprise

Converged Concept 1: The Convergence of DSPM and Discovery-Led Truth Layers

• System of Record: Discovery-led classification (and label translation and synchronization between Google and Microsoft environments) establishes the definitive truth layer, where Data Security Posture Management (DSPM) acts as the system of record to position DLP as a measurable outcome.

• New Architectural Model: DSPM (inventory + classification + risk) → data control plane (orchestration) → distributed enforcement points.

Converged Concept 2: Adaptive and Contextual Data Control Planes

• Operational Goal: Reducing the operational tax and eliminating ticket factory outcomes through high-fidelity signal prioritization and automated triage.

• Contextual Governance: Policies must integrate identity, access entitlements, and sharing posture to inform automated remediation and prevention across the full data lifecycle.

Converged Concept 3: Securing the AI Runtime: Prompts, Copilots, and Agents

• Leakage Modes: Addressing emerging exposure paths, including prompt-based exfiltration, Copilot access scope, and agentic tool-call actions across cloud and SaaS surfaces.

• Runtime Controls: Implementing session-based interventions—such as redaction, masking, and policy-driven warnings—tailored to specific data sensitivity and user behavior.

• Auditability: Establishing governance through audit-grade evidence, capturing data lineage and event provenance to support forensic investigations and remediation or prevention proof.

The AI-era Example Scenario

The New Scenario: An employee pastes proprietary code into a public GenAI chatbot

Modern DLCP response: Move towards warning users or redacting sensitive fragments and log an event with audit-grade detail

1. Technical description: Prompt inspection, redaction, policy-driven warnings and blocking, Copilot governance, agent tool-call control and logging.

2. How it addresses the problem: Covers exposure modes that do not resemble classic file and email exfiltrations.

3. Integration considerations: Needs enhanced integrations into GenAI surfaces including but not limited to AI chat interfaces, Developer API Surfaces, Integrated AI services and AI Agent platforms and providers with strong logging and evidence as a differentiator.

A Deep Dive of the Stack: Modern DLP Layers 0 through 6

Intelligence Plane

Layer 1: Continuous discovery & classification (the truth layer)

• Technical description: API-based scanning of SaaS repositories and cloud data stores and performs classification using advanced patterns and machine learning (ML) algorithms with continuous posture updates to reflect real-time changes.

• How it addresses the problem: This layer reduces the unknown-unknowns that typically drive the noisy enforcement environments of the past, sporting high false-positive rates, and drawing political pushback from business units. By establishing a definitive truth layer through Data Security Posture Management (DSPM), organizations can position DLP as a measurable outcome of discovery and business enabler or at least an optimizing function.

• Integration considerations: Coverage breadth is heavily dependent on the quality of vendor connectors across SaaS, IaaS, and on-premises environments. High-fidelity classification and robust evidence trails are critical for supporting forensic investigations and establishing stakeholder trust in automated actions.

• Scanning Performance, Cloud Costs and Speed to Discovery: Various styles of deployment and sampling rates dictate the speed of discovery and scanning. For example, with file scanning using centralized methods, this can impact performance. Scan speed can be enhanced based on localized cloud based scanners and sampling of data stores (especially for structured data types) can be essential for speed (for example, identifying a credit card bin or social security number by sampling a few rows in a database table vs examining all data). Any DLP scanning interactions from Cloud to Cloud or Cloud to Premise can expand cloud costs.

Layer 2: Access and usage context (identity, entitlement, posture and behavior)

• Technical description: Modern DLP can augment content findings with who-accessed and who-shared, privilege and entitlement context, sharing posture, and sometimes lineage.

• How it addresses the problem: Applying these contextual elements, especially through federated context facilities such as model context protocol (MCP) reduces false positives and can enhance speed due to reduced re-classification need and enables prioritization or data security controls and enforcement mechanisms (what matters, to whom, and why).

• Integration considerations: This layer requires identity and SSO signals, SaaS audit logs, and in some cases endpoint or network gateway/SASE/SSE telemetry.

Layer 3: Policy orchestration across tools (the data control plane)

• Technical description: Ideal deployments will offer the ability to centralize data security policy definitions that map to distributed enforcement points and enforcement templates and apply these suggested policies to reduce manual burden.

• How it addresses the problem: Can help prevent policy sprawl across tools and aligns controls to business context and data labeling.

• Integration considerations: Orchestration depth varies, many vendors still are reliant on single vendor selection, but some products push policy into SSE and SASE or endpoint, others focus on SaaS actions.

Enforcement Plane

Layer 4: Enforcement planes (API, inline, browser/session, endpoint)

• Technical description:

  • API/SaaS-first: Out-of-band detection + actions (quarantine, redact, revoke links)

  • Inline SSE/SASE/GW: Offers real-time inspection/control for web and SaaS traffic (often requires steering and SSL/TLS decryption)

  • Browser/session (last-mile runtime): In-session control over copy/paste, upload/download, printing, screen capture, and GenAI interactions

  • Endpoint: device-level controls for local exfil paths (removable media, local copies, unmanaged sync clients, print, clipboard)

• How it addresses the problem: Provides practical control coverage across the actual runtime surfaces where data moves.

• Integration considerations: Inline requires steering, endpoint requires rollout and tuning, browsers may require extension or browser replacement.

Layer 5: Automated remediation and prevention workflows

• Technical description: Automated responses (warn, redact, delete, revoke sharing, label) plus audit trails and investigation context.

• How it addresses the problem: Shifts DLP from alert factory to measurable risk reduction and autonomous prevention.

• Integration considerations: Action depth is vendor- and connector-dependent, evidence quality is crucial for stakeholder trust.

Layer 6: AI runtime DLP (prompts, copilots, agents)

Capability taxonomy: AI runtime DLP

Emerging capability often delivered via API surface or inline proxy runtime) see also SACR Unified Agentic Defense Platforms publication (UADP).

• Prompt input controls: Offers paste/upload inspection, warnings, redaction/masking, and policy-driven blocking for sensitive inputs.

• Output controls: Offer redaction/masking/watermarking of generated outputs and safe copy or data export controls.

• Tool-call governance : Can constrain what tools can access/send, enforce least-privilege data access, and log tool inputs and outputs where appropriate, controls data residency and trust.

• Residency and compliance guardrails: Residency of data and compliance guardrails must exist for the data they inspect and log. This is critical for regulated environments where PII/PHI/PCI data cannot cross geopolitical or operational boundaries, even when processed by an agent or a cloud-hosted tool.

• Evidence and forensics for agent actions: Chain-of-custody events for agent activity (who/what/when), provenance, and investigation-ready timelines.

• Technical description: Prompt inspection,redaction, policy-driven warnings/blocks, Copilot governance, agent tool-call logging.

• How it addresses the problem: Covers exposure modes that do not resemble classic file/email exfiltration.

• Integration considerations: Needs integration into GenAI surfaces, strong logging and evidence is a differentiator.

Enterprise Data Loss Prevention Great DLP Reset Deployment Framework (Ideal Scenario)

Market landscape: DLP layers (archetypes)

This section maps common product layer archetypes to the control-plane model. Many platforms span multiple layers, but most have a primary center of gravity.

Adoption path (maturity model)

1. Start with the truth layer: Can establish discovery and classification and basic evidence.

2. Prove remediation and prevention in one high-noise channel: Pick a SaaS surface and close the loop with reversible actions.

3. Add context to reduce noise: Add context from sources such as identity,entitlements and sharing posture to prioritize what matters most for preventive enforcement actions or remediation.

4. Add heavier enforcement intentionally: Inline SSE/SASE/GW (and SSL/TLS decryption) and endpoint where required by risk scenarios.

5. Make AI runtime governance explicit: Emerging prompt,output, tool-call controls offer enforcement of DLP policy plus may add AI agent evidence through MCP monitoring or API integrations. Treat browser and sessions as a primary runtime where applicable. Also see SACR publication (Unified Agentic Defense Platforms (UADP))

6. To help Unify Enforcement Policies: Develop and use Normalized Data Sensitivity Labeling across Enforcement Points. Data sensitivity labeling from DSPM serves as a ground truth layer, derived from Microsoft Purview and Google Workspace classification label schemes.

Data Sensitivity Labeling: Normalized for Enforcement Layer Policies

What DLP Vendors Should do to Win in the Great DLP Reset

The DLP market is being re-oriented by buyers against a new baseline: measurable risk reduction with lower operational burden, across modern runtimes (SaaS, cloud data platforms, and GenAI). Vendors that continue to lead with more detections without proving enforceable outcomes will increasingly be treated as noise generators rather than control-plane platforms.

1) Make operational burden a first-class product outcome

Buyers now treat deployment complexity, policy sprawl, and false-positive triage as existential program risks. Vendors should:

• Ship opinionated defaults (starter policies, templates, and tuning guardrails) rather than assuming every customer will build a program from scratch.

• Provide staged rollout and rollback mechanics that are predictable and safe (preflight checks, safe modes, clear blast-radius controls).

• Instrument and report burden: time-to-deploy, time-to-first-signal, false-positive rate, triage hours/week, and disruption rate (how often enforcement breaks legitimate work).

2) Prove time-to-first-signal and prevention in days or hours, not quarters

A major separation in the market is how fast a platform can surface “material risk” (not just matches). Vendors should design onboarding around a 1–2 week proof window, sooner if at all possible:

• Fast connectors to core data gravity (M365/Google, Slack, Salesforce, GitHub, key cloud stores).

• Immediate prioritization (what’s sensitive, who can access it, what’s externally exposed).

• At least one closed-loop remediation and prevention enforcement paths early (revoke public links, quarantine, ticket, redact, block, delete) so visibility becomes risk reduction.

3) Turn context and LMM natural language into a real differentiation lever

Modern DLP decisions are increasingly identity and entitlement-driven. Vendors should operationalize context:

• Identity, entitlements and sharing posture should directly reduce noise and improve prioritization.

• Explanations must be human-usable: why this object mattered, why this actor and action is risky, and what changed after remediation.

• Enhancing Insider Threat incidents with natural language cognitive Large Language Model (LLM) outputs can significantly increase storyline elaboration on incidents and events.

4) Be explicit about enforcement points, and avoid one-control-point narratives

Buyers are increasingly skeptical of vendors that imply universal coverage from a single enforcement surface. Vendors should clearly articulate:

• Where controls actually execute (SaaS/API, inline SSE/SASE/GW, endpoint, browser/session, email).

• Which actions are enforceable per channel (block, quarantine, revoke sharing, redact/delete, label/classify, coach/warn, ticket/workflow).

• How policy intent stays consistent across distributed control points (a real control plane vs. disconnected features).

• How they properly stitch together events and data from various sources across the DLP overall deployment architecture to properly create incidents and perform remediation actions in-context, or nudges to users without creating fatigue.

5) Win on remediation depth (with guardrails), not alert volume

The market is shifting from find to fix, nudge,inform. Vendors should strengthen remediation and prevention depth and safety and their ability to lightly engage users:

• Prioritize reversible and low-friction actions first (revoke sharing, quarantine, remove public access) before heavy blocking.

• Provide automation with guardrails (approvals, exception handling, rollback, and proof-of-remediation and prevention).

• Track outcomes that matter: exposure reduction, % auto-remediated, MTTR, and repeat-offender reduction.

• Engage users lightly through agentic communications via communications channels (Slack, Teams, etc) for light nudge and user contextual education.

6) Treat GenAI and agent platforms as a default runtime

GenAI and agent platforms introduce high-frequency leakage paths (prompts, uploads, outputs) and emerging MCP agent/tool-call surfaces. Vendors should:

• Ship concrete prompt/output controls (detect, warn, redact, block) based on sensitivity and context.

• Provide audit-grade logging for GenAI interactions, including relevant inputs/outputs where feasible and permitted.

• Package GenAI policies as templates aligned to real data types (source code, credentials/secrets, regulated identifiers, contracts/M&A).

• Consider and Expand integrations and capabilities towards Agentic Workflow and agentic platforms (See consideration list for supported data loss controls in the mapping below)

7) Raise the evidence bar with: audit-grade, investigation-ready artifacts

As DLP becomes a control plane, evidence and chain-of-custody become competitive wedges. Vendors should:

• Attach defensible evidence to each high-impact event: actor, object, action, sensitivity, destination, timestamps, and remediation or prevention result.

• Provide investigation-ready timelines and, where possible, lineage/provenance signals that support incident response and audit readiness.

8) Leverage standardized labeling, and seek cross-product integrated enforcement

Since customers increasingly utilize various vendors in their data loss prevention and data security programs, it’s incumbent on existing vendors to work more harmoniously together, sharing enough information properly to improve the enforcement and control layers. Vendors should:

• Integrate or develop sharing mechanisms between discovery and control planes to properly utilize standardized labeling schemes if this capability is not already present.

• Consider leveraging the Microsoft security graph (as an example) and other sources of risk information as common context sources for enforcement or as elevated risk signals

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Market Competitors: Data Loss Prevention (DLP)

Key Vendor Differentiators:

Automation depth, Audit-grade evidence, Data Lineage, Closed-loop Autonomous Tuning

Great Reset Vendor Alignment Archetypes delivering Modern DLP capabilities:

• API / SaaS-first

• Inline SSE/SASE/GW

• Endpoint agent

• Browser extensions

• Enterprise browsers

• DSPM-led control plane

• Hybrid

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Notable Vendor Profiles

CrowdStrike

Vendor Profile

CrowdStrike’s center of gravity in DLP is as a data security solution embedded into the broader Falcon security platform, spanning endpoint, SaaS, and cloud data security, including real-time visibility and control of data movement across environments and insider-risk investigation workflows. In practice, CrowdStrike tends to be evaluated when buyers want to consolidate security telemetry and response in a single operating console, reduce tool sprawl, and connect data movement events to identity, device posture, and threat activity, rather than stand up a standalone, multi-channel enterprise DLP suite from scratch. CrowdStrike is an integrated data security capability within the Falcon platform, including DLP and DSPM capabilities for data at rest and extending them with real-time visibility into data in motion across endpoint, SaaS, and cloud environments.

Products/Services Overview

• Falcon platform-delivered data security module(s) Intended to detect and control sensitive data movement across endpoints, SaaS applications, and cloud environments

• Insider-risk oriented workflows that make who did what with data investigable across endpoint, SaaS, and cloud environments, alongside endpoint, identity and cloud telemetry (useful for investigations, response, and policy exception handling).

• Platform-driven integration approach: Data security events and detections across endpoint, SaaS, and cloud environments are designed to be consumed in the same operational plane as endpoint security, identity protection, and broader detection and response functions (reduces swivel-chair across tools).

• Policy-driven controls and reporting/audit features appropriate for enterprise security operations.

Market Category

DLP / Data Security Platform (DSP)

Market Sub-Category

Insider Risk / DDR

Great DLP Reset Alignment

Hybrid

CrowdStrike aligns to the Great DLP Reset less as a single DLP product and more as a consolidation-driven layer that connects data movement risk to identity, endpoint posture, cloud posture and response operations. In the Reset framing, where DLP evolves into a unified data control plane, CrowdStrike’s role is typically strongest on the investigation and response side and enforcement at endpoint egress as well as visibility and control across SaaS and cloud environments, helping security teams reduce time-to-triage by correlating data loss and data movement events with broader threat and user behavior context. The main tradeoff is architectural as teams seeking first-class SaaS-first/API remediation or deep DSPM-style truth layer and discovery may treat CrowdStrike as complementary rather than primary, while Falcon-centric organizations may prioritize it as the operational backbone that unifies security actions.

Core Functions and Use Cases

• Cross environment data loss visibility: understand and govern sensitive data at rest and data movement across endpoints, SaaS applications, and cloud services..

• Insider risk investigation and response: Detect anomalous behavior and connect suspicious data movements to identity, device posture, and security events. Supports event based forensic screen captures to provide full context of detection.

• Operational consolidation: Bring data security signals into a single SecOps operating plane to accelerate triage and response.

• Policy-driven monitoring and guardrails: Establish baseline monitoring and targeted preventative controls for high-risk scenarios.

Use Cases and Pain Points Addressed

1. Detect and investigate suspicious file movement by employees or contractors

Detect anomalous behavior and connect data movement events to identity and endpoint context to reduce investigation time and improve defensibility. Supports event based forensic screen capture to provide full context of detection.

2. Reduce the swivel chair, multi-console approach during data incident response

Centralize data-related events where SecOps teams already work, reducing handoffs between endpoint, SIEM, and standalone DLP tools.

3. Targeted prevention for high-risk exfiltration paths across endpoints, SaaS applications, and cloud environments.

Apply policy guardrails to the data movement patterns that commonly show up in insider scenarios (scope depends on enabled controls).

4. Support audit and incident review workflows

Preserve a clearer narrative of what occurred, by whom, and on which endpoint(s), enabling more consistent post-incident reporting.

5. Rationalize overlapping tools in platform-consolidation programs

Where buyers have multiple partial controls (endpoint + SSE + M365), CrowdStrike can function as the operational glue for triage and response, though not necessarily the deepest enforcement layer everywhere.

Differentiation and Competitive Novelty

• Strongest differentiation is operational consolidation: Aligns data security events with endpoint and identity telemetry in a single platform experience.

• Investigation-first posture: Often well-suited to insider-risk-heavy requirements where proving intent, tracing activity, and accelerating response are as important as blocking.

• Platform adoption leverage: Can be compelling when Falcon is already deployed widely, lowering friction compared to introducing a new, standalone DLP management plane.

SACR Key take away:

CrowdStrike is best suited for organizations prioritizing consolidation of data security within a broader security platform, particularly where connecting data movement, user activity, and response workflows is important. It is typically evaluated in scenarios focused on insider risk investigations and operational unification, especially in environments with an existing Falcon deployment, rather than as a standalone, multi-channel enterprise DLP platform.

Cyberhaven

Vendor Profile

Cyberhaven is a data detection & response (DDR) platform built around data lineage. The core thesis is that classic DLP fails at modern workflows because content-only inspection lacks context. Cyberhaven finds and follows data through endpoints and browser activity to determine where it came from, how it was transformed, and what a user is trying to do with it. In practice, Cyberhaven is best understood as a last-mile enforcement and investigation layer for DLP programs: it focuses on data-in-use (copy/paste, uploads/downloads, sharing actions) and produces richer evidence for investigations and insider-risk scenarios.

Products/Services Overview

• Data Detection & Response (DDR) platform: A lineage-centric platform that finds and follows data through endpoints and browser activity to determine provenance and intent.

• Reimagined DLP: Provides prevention and policy enforcement based on data lineage and context rather than content-only inspection.

• Insider Risk Management: Combines behavioral signals with data lineage to identify and resolve attribution of persistent IP leakage.

• AI security capabilities: Delivers inventory and controls for AI tools and agent-like workflows, addressing data recombination and exfiltration at scale.

Market Category

Insider Risk / DDR

Great DLP Reset Alignment

Hybrid

Cyberhaven aligns with the Great DLP Reset as a last-mile enforcement and investigation layer that preserves the truth of sensitive provenance through data lineage, even as data is modified. Architecturally, it deploys across endpoint agents and browser extensions to capture granular in-use activities. The tradeoff is that while it produces significantly richer evidence for forensic investigations and reduces alert fatigue via context-aware decisions, it carries the higher operational burden associated with endpoint and browser-mediated deployments compared to API-first models.

Core Functions and Use Cases

1. Lineage-driven detection and classification

   • Track data objects and derivatives through user workflows to preserve provenance and intent context.

2. Prevent exfiltration in data-in-use paths

   • Focus on last-mile actions: copy/paste, upload/download, email/web destinations, and other endpoint/browser mediated movements.

3. Insider risk and investigations

   • Use lineage to accelerate investigation timelines and reduce “swivel-chair” work.

   • Example from briefing: Cyberhaven described supporting investigations where the goal is to identify the source of persistent IP leakage and resolve attribution (e.g., find a mole scenario).

4. AI-era leakage paths

   • Cyberhaven described AI as a major driver of demand, even for organizations that are not AI-forward, because AI accelerates data recombination and exfil at scale.

Use Cases and Pain Points Addressed

Differentiation and Competitive Novelty

• Data lineage as the core primitive (not just a feature): lineage is used to inform classification, enforcement decisions, and evidence generation.

• Context-first enforcement model: emphasis on deciding when to block vs warn vs allow using provenance + identity and behavior signals, not only content patterns.

• Strong fit for existential data scenarios: advanced manufacturing / product design, frontier AI labs, and highly regulated environments where data is the business.

SACR Key take away:

Cyberhaven is best positioned for organizations where data is the business, such as frontier AI labs, advanced manufacturing, or highly regulated sectors requiring deep forensic proof of intent. Shortlist Cyberhaven when your threat model requires tracking data derivative fragments through complex user workflows and you are prepared to operationalize endpoint/browser telemetry to achieve investigation-ready timelines.

Cyera

Vendor Profile

Cyera is a data security platform vendor that has expanded from DSPM (sensitive data discovery, classification, and exposure analysis) into DLP via Omni DLP, positioned as an agentic intelligence layer that correlates and enriches DLP signals from existing enforcement tools rather than replacing them outright. In DLP terms, Cyera’s center of gravity is improving time-to-triage and policy confidence by adding data sensitivity and access context (data at rest truth) to data in motion and in use events coming from email/web/SSE/endpoint, SaaS, and AI ecosystems.

Products/Services Overview

• DSPM / data security platform foundation: Agentless discovery and classification of sensitive data across cloud/SaaS/hybrid/on-prem data stores, plus exposure and access analysis to establish what data exists, where, and who can reach it.

• Omni DLP (DLP module): Agentic intelligence layer for aggregating DLP events from existing tools, enriching with Cyera data context, and prioritizing and triaging alerts, positioned as sitting above existing DLP controls (not a rip-and-replace).

• Browser Shield (AI module): Browser extension for mapping AI footprint, resolving session-level identity, and enforcing real-time blocking by intercepting prompts to analyze conversational context and intent before data is ever transmitted to public LLMs, sanctioned copilots, or emerging AI agents.

• Policy and tuning assistance: Guidance on which policies are noisy vs. high-signal and recommendations intended to help teams move from monitor-only to safer enforcement over time. Orchestrates policies to achieve the same goals in different technical silos.

• Remediation workflow support (platform-wide): One-click or guided remediation actions and integrations with ticketing / SOAR-style workflow tools (e.g., ServiceNow/Jira and automation partners referenced by Cyera) to operationalize findings.

• Integrations ecosystem: Published integrations and partner ecosystem intended to connect data context to downstream security and operations tools.

Market Category

Insider Risk / DSPM / Data and AI Security Platform / DLP

Great DLP Reset alignment

Hybrid

Cyera aligns to the reset narrative by treating classic DLP as a fragmented set of enforcement points that struggles without a strong truth layer (sensitive-data understanding and access context) and without an operationally efficient way to reduce noise. In practice, Cyera’s model is to establish high-confidence data context via DSPM, ingest and normalize signals from existing DLP enforcement controls, then use that context to drive prioritization, tuning recommendations, and targeted remediation workflows. The tradeoff is architectural dependency and value is highest when Cyera can integrate broadly into the enforcement and telemetry sources already deployed.

Core Functions and Use Cases

• Sensitive data discovery and classification as DLP prerequisite: Build an inventory of sensitive data and its locations to drive precise DLP scoping.

• Cross-channel DLP signal rationalization: Reduce alert flooding of the SOC and data owners by correlating and enriching events with data and access context and highlighting the subset most likely to represent meaningful risk.

• Policy improvement workflow: Identify noisy policies and provide tuning guidance to improve confidence before enforcement changes.

• Exposure-driven remediation: Route concrete remediation (remove public links, reduce over-sharing, reduce risky access paths) to the right owners with auditability.

• AI-era data loss governance: Apply the same data context and runtime signals concept to AI-related data interactions.

  • Shadow AI discovery: Inventory every AI tool accessed via managed browsers, move from unsanctioned to approved

  • AI prompt protection: Block user prompts that leak sensitive data or violate acceptable use policy, including malicious content, in browser-based sessions

Use Cases and Pain Points Addressed

1. Reduce false positive triage load in existing DLP programs by adding data sensitivity and access context to alerts, so analysts can focus on fewer, higher-confidence events (enables faster time-to-decision).

2. Make monitor-only policies safer to operationalize by measuring where rules produce noise and guiding tuning toward higher precision (reduces business disruption risk when moving toward blocking).

3. Prioritize remediation based on business risk context (where sensitive data is, who can access it, and how it is being shared/exposed), enabling targeted cleanup (revoke access, fix sharing posture, ticket to owners).

4. Speed up scoping during investigations by connecting where sensitive data lives to what event happened (reduces time spent chasing owners and data lineage manually).

5. Integrate data risk signals into existing IT/security workflows (ticketing/automation) so remediation doesn’t require a new operational process for every finding.

Differentiation and Competitive Novelty

• Brain over the stack positioning: Omni DLP is explicitly framed as augmenting, not replacing, existing DLP enforcement points, which can fit enterprises that already standardized on Microsoft,SSE/SASE,email,endpoint and SaaS controls.

• DSPM-to-DLP linkage: Uses sensitive data discovery and classification and access context as a first-class input to DLP alert quality and prioritization (differentiates vs. DLP approaches that rely primarily on inline content inspection without strong enterprise-wide data inventory).

• Operational emphasis on triage and tuning cycles: Compresses tuning cycles and improves confidence in enforcement decisions by using richer context and analytics over alerts/policies.

• Platform remediation orientation: Converting findings into actions via guardrails and audit trails and workflow integrations rather than stopping at visibility.

SACR Key take away:

Cyera is best fit for CISOs who already have meaningful DLP enforcement deployed (Microsoft/SSE/email/endpoint/SaaS controls) but are dissatisfied with signal quality, triage workload, and the lack of data-context-driven prioritization OR are starting their DLP program and seeking to establish baseline policies without incurring long tuning cycles. Shortlist Cyera when the goal is to make DLP operationally viable by anchoring decisions in sensitive data truth (discovery and classification and access context) and orchestrating remediation through existing workflows, rather than pursuing a single-vendor rip-and-replace.

Microsoft

(Microsoft Purview DLP)

Vendor Profile

Microsoft’s DLP capabilities are primarily delivered as part of the Purview data security platform positioned as the default data protection control for Microsoft 365–centric enterprises that need to reduce accidental oversharing and policy violations across collaboration, email, and endpoints. Its center of gravity in DLP is native platform DLP which uses a centralized policy model administered in Purview, applied across core Microsoft 365 workloads (e.g., Exchange, SharePoint, OneDrive, Teams, Agent 365, Copilot) and extended to endpoints and some browser-mediated workflows via Endpoint DLP and related browser capabilities. It positions this as a way to build a layered protection strategy that spans cloud, endpoint, browser, and network. It addresses M365 environments by being built directly into its productivity suite, Purview is also one of the only solutions that works within the compliance boundaries of that productivity suite. In the Great DLP Reset framing, Microsoft is strongest when the environment is already anchored in Microsoft identity, productivity, and compliance workflows, while cross-SaaS and non-Microsoft enforcement breadth (although it has consistently expanded for the last 2 years) can require additional products or complementary vendors.

Products/Services Overview

• Microsoft Purview Data Loss Prevention: Centralized creation and management of DLP policies, applied across supported Microsoft 365 locations, on-prem file shares and Microsoft Fabric for structured data and user activities; uses deep content analysis and policy actions (conditions and actions) for protection and control. Purview now includes M365, endpoints, Fabric, Copilot, on-prem file shares, browsers, networks, and Copilot Studio/Foundry-built agents.

• Microsoft 365 workload DLP (Exchange, SharePoint, OneDrive, Teams): DLP coverage across Microsoft 365 collaboration and messaging surfaces in Teams support which includes chat and channel messages (including private channels) under specific licensing, and Teams file-sharing inherits SharePoint and OneDrive controls because of how Teams stores files.

• Endpoint DLP (Part of overall Microsoft Purview Data Loss Prevention): Extends DLP monitoring and enforcement to Windows 10/11 and macOS (latest major versions) devices, Windows servers and network shares once onboarded, with visibility in Activity Explorer and enforcement through DLP policies.

• Edge for Business for Cloud Apps: Inline data loss prevention is directly built into Edge for Business for real-time text/file upload scenarios, such as AI prompts & responses; it also supports data protection controls for unmanaged device and BYOD scenarios via Edge.

• Browser-related extension and controls: Microsoft Purview extensions to extend Endpoint DLP capabilities into Edge, Chrome, Safari and Firefox on Windows devices.

• DLP for Copilot interactions: Microsoft provides capabilities to apply Purview DLP to protect interactions with Microsoft 365 Copilot and Copilot Chat, Copilot Studio Agents as well as pre-built agents in Copilot.

• Shared classification foundations (within Purview): DLP conditions can incorporate Microsoft Purview Information Protection elements such as sensitive information types (including out-of-box and customer-defined) and trainable classifiers (Names Entities, EDM, Fingerprinting, OCR, etc) in supported locations.

Market Category

Integrated DLP

Market Sub-Category

Productivity-suite/native platform

Great DLP Reset alignment

Hybrid

Microsoft aligns to the Great DLP Reset primarily as a native control plane with multiple enforcement points anchored in the Microsoft estate. It represents the pragmatic reset path for Microsoft-first enterprises: leverage existing classification, compliance administration, and identity context to apply DLP policy across collaboration and messaging, then extend to endpoints and browser workflows. The main tradeoff is that while Microsoft can deliver fast value inside the M365 boundary, modern DLP programs frequently need consistent enforcement and remediation across non-Microsoft SaaS, cloud data platforms, and new AI workflows; native integration with both secure browsers and SSE/SASE(which Microsoft supports) to detect and intercept sensitive data in flight to 3rd party AI apps (at the web traffic layer). Those broader requirements can increase complexity or drive a fill the gaps strategy with SSE/SASE, SaaS-first remediation, or DSPM-led tools. With Enterprise AI Microsoft offers built-in data security and compliance controls for M365 Copilot, Copilot Chat, Copilot Studio-built agents, and Foundry-built agents; adoptees can also choose to leverage their SDK to extend Purview data security and compliance controls to their own proprietary apps and agents.

Core Functions and Use Cases

• Microsoft 365 collaboration and email DLP: Reducing oversharing and inappropriate transmission of sensitive data in Exchange and collaboration repositories, with enforcement tied to content and policy conditions.

• Endpoint data-in-use visibility and control: Monitoring user actions on sensitive items on managed endpoints and enforcing protective actions via DLP policies once devices are onboarded.

• Teams message and file-sharing governance: Controlling sensitive data sharing in Teams chats and channels and files shared via Teams (leveraging SharePoint and OneDrive storage models).

• Data protection in the browser via inline & endpoint DLP: Detecting and blocking sensitive text or files uploading in Edge for Business using built-in inline data protection controls, without extensions or device onboarding. Extending enforcement and monitoring for sensitive item access, pasting, or uploading capabilities (for example cloud-app DLP scenarios) in Windows via the Purview extension for organizations using Edge, Chrome or Safari.

• AI-era coverage in Microsoft surfaces: Applying DLP policy to interactions with Microsoft 365 Copilot, Copilot Chat, and Copilot Studio agents. The Purview SDK is available to extend Purview policies to proprietary or custom-built AI apps & agents

Use Cases and Pain Points Addressed

1. Preventing regulated data sharing via Exchange email: Use of DLP policy conditions and actions for Exchange to identify sensitive items and enforce outcomes matters because email remains a high-frequency exfiltration channel with strong compliance implications.

2. Reducing oversharing in SharePoint, OneDrive, Teams file workflows: Apply DLP to documents in collaboration stores and the sharing workflows that expose them matters because collaboration-first is a primary leakage mode.

3. Monitoring and preventing risky endpoint actions on sensitive items: Endpoint DLP extends DLP from cloud workloads to local device behavior and provides centralized visibility in Activity Explorer (now available directly in DSPM) which matters for data-in-use risks and local exfiltration paths.

4. Extending policy to browser upload and access attempts on Windows: Using the Purview extension to monitor and enforce attempts to access or upload sensitive items to cloud services in Chrome matters because so many SaaS interactions happen in the Chrome browser even in very Microsoft-centric environments. Microsoft can also block inline text/file uploads in the Chrome browser (including prompts and responses) via network DLP. Microsoft can also apply differentiated protections on endpoints for apps, web domains and peripheral devices like USB, printers.

5. Applying DLP to Copilot interactions: Using Purview DLP to protect interactions with Microsoft 365 Copilot and Copilot Chat matters because AI workflows introduce non-file leakage paths that often sit inside productivity surfaces.

Differentiation and Competitive Novelty

• Ecosystem-native control plane: Purview’s differentiation is tight integration with the Microsoft 365 administrative and compliance plane, letting Microsoft-first organizations govern core collaboration and messaging surfaces without introducing a separate DLP stack for those channels.

• Unified policy administration across multiple Microsoft locations: Microsoft emphasizes centralized policy management and unified alerting and remediation within the Purview portal for buyers, the practical value is reduced console sprawl inside the Microsoft estate.

• Broad coverage across enterprise apps and devices when standardized on Microsoft Purview where DLP spans Microsoft 365 plus endpoints (Windows and macOS) and browser pathways. This contrasts with point products that begin as SaaS-first remediation or browser-only enforcement.

• Integration with adjacent Purview capabilities: Microsoft highlights alignment with Information Protection (labels and SITs) and Insider Risk Management (adaptive protection concepts), which can be attractive where governance and risk programs are already Microsoft-aligned.

SACR Key take away:

Microsoft Purview DLP is a strong default shortlist for Microsoft–centric organizations that want fast, centralized policy control across Exchange, SharePoint, OneDrive, and Teams, with a clear path to extend controls to endpoints and selected browser workflows. It tends to be most effective when the organization accepts Microsoft’s governance model and can operationalize endpoint onboarding and sustained policy tuning.

Netskope

Vendor Profile

Netskope is a cloud security platform vendor best known for Security Service Edge (SSE/SASE) capabilities (secure web gateway, CASB, ZTNA) with DLP embedded as a core control for protecting sensitive data moving across web and cloud application traffic (In-Motion and At-Rest). In DLP, Netskope’s center of gravity is inline enforcement (real-time control when traffic is steered through Netskope) combined with API-based SaaS and IaaS controls (out-of-band scanning and remediation for sanctioned apps). This dual-mode approach targets a practical enterprise reality where some DLP outcomes require real-time blocking (e.g., upload prevention) or coaching, while other outcomes prioritize fast deployment and at-rest governance in SaaS via APIs.

Products/Services Overview

• Cloud-delivered DLP (within Netskope One / Intelligent SSE): Content inspection and policy enforcement for sensitive data moving through web and cloud app traffic, typically administered as part of the broader SSE control plane.

• Inline DLP enforcement (proxy-based): Real-time inspection and control for web and SaaS transactions when traffic is routed through Netskope (forward and/or reverse proxy patterns).

• API-based or Connector based for SaaS, IaaS and OnPrem datastores: Out-of-band API and connectors into sanctioned SaaS, IaaS and on-premises data stores and services to discover and classify data at rest, detect policy violations, and take policy actions (noting that enforcement is inherently after-the-fact versus inline).

• DLP detection techniques: Support for multiple detection approaches such as dictionaries/identifiers, proximity analysis, OCR, fingerprinting for text and images as well as File classifiers for a set of images and texts (i.e. source code or passports), and exact match style approaches.

• Policy action set: actions can include alerting and various remediation controls including user coaching, adjusting permissions, or protection with MIP.

• Endpoint DLP (as an add-on capability): Endpoint-focused controls for preventing sensitive content transfer to channels such as USB storage devices, printers, Bluetooth, and similar device-control scenarios, positioned as leveraging Netskope’s DLP profiles and rules with endpoint control policies.

• Email DLP (as an add-on capability): Focus on SMTP email traffic leveraging Netskope’s DLP profiles and rules to safeguard data.

• AI security / AI Gateway (adjacent): AI Gateway and Agentic Broker that can apply DLP as part of policy enforcement for app-to-app (i.e. MCP) traffic between AI agents, apps and LLMs (deployable as a virtual appliance). This is relevant when buyers want DLP policies to extend into AI usage patterns.

• Data Lineage (as an add-on capability): Provides comprehensive visibility into the provenance, movement, and usage of data across cloud, web, endpoint, and AI applications visually with data lineage graph.

• DLP On Demand: Allows developers via REST APIs to integrate data protection into custom apps and workloads. It supports secure, local processing of structured/unstructured data. Deployment options include an appliance in IaaS (AWS, Azure, GCP) or on-premises (VMware, Hyper-V, KVM).

Market Category

Integrated DLP

Market Sub-Category

SSE/SASE-integrated

Great DLP Reset Alignment

Hybrid

Netskope aligns to the Great DLP Reset as a distributed enforcement vendor anchored in SSE/SASE as it provides a real-time enforcement plane via inline proxy control for web and SaaS traffic, while also offering out-of-band SaaS and API governance for data at rest and collaboration risk in sanctioned applications, IaaS and on-premises repositories. This maps to the reset’s core tradeoff: inline enforcement can deliver strong prevention outcomes but introduces steering/decryption and ongoing tuning burdens but API-based SaaS controls can be faster to deploy and useful for at-rest remediation, but inherently have after-the-fact characteristics and app-dependent action depth. This is complemented by Netskope DLP Endpoint, DLP Email and the integration of DLP into Enterprise Browser to provide deployment and enforcement options. Netskope’s architecture is often most compelling when the organization wants a single policy narrative spanning both modes as well as a unified view on data and incident management.

Core Functions and Use Cases

• Real-time prevention for web,SaaS and email traffic: Stopping or controlling sensitive data movement during in-flight web/SaaS/email interactions (uploads, posts and downloads) where real-time outcomes are needed.

• SaaS/API/IaaS/On-Premises governance and remediation: Scanning content already resident in sanctioned SaaS services, identifying sharing violations or sensitive content exposure, and executing supported remediation actions.

• Unified policy administration across enforcement modes: Applying consistent data protection policies across both inline and API surfaces to reduce policy fragmentation.

• Endpoint-adjacent controls (select scenarios): Preventing sensitive content from moving to removable media or peripheral channels (USB/print/Bluetooth), for organizations that want endpoint controls under the same security umbrella.

• AI-era data controls (where adopted): Extending data protection intent into AI usage patterns via Netskope’s AI security constructs (requires careful scoping and many buyers treat this as a separate workstream).

Use Cases and Pain Points Addressed

1. Blocking sensitive uploads to cloud storage or SaaS in real time: Inline enforcement can prevent an upload before it completes, reducing the exposure window that exists in out-of-band API scanning models. This matters for high-risk workflows where detect and remediate later is not acceptable. Enforcement spans to Enterprise Browser, ZTNA, Cloud Firewall and Email.

2. Detecting and remediating sensitive data at rest in sanctioned SaaS apps: API connections can scan existing content and enforce policy actions within supported apps, which matters for collaboration sprawl and legacy content already present in SaaS repositories.

3. Reducing false positives for regulated identifiers via higher-precision matching options: Techniques such as exact match datasets and fingerprinting are designed to reduce over-triggering in environments where pattern matches alone are noisy.

4. Controlling exfiltration to removable media and peripherals: Endpoint DLP use cases around USB storage and peripheral channels matter in regulated industries and insider-risk scenarios where local exfiltration paths remain a top concern.

5. Coaching and justification (nudge) patterns for unsanctioned app usage (DLP-adjacent governance): Offers policy-driven user coaching and justifications for accessing unsanctioned apps.Which complement DLP since it can reduce shadow IT pathways that become data-loss channels.

Differentiation and Competitive Novelty

• Dual-mode SSE DLP strategy: The combination of inline real-time enforcement plus API-based SaaS governance allows buyers to choose enforcement surfaces by risk scenario and operational feasibility.

• Large-scale private edge infrastructure (NewEdge): Netskope’s ability to run real-time security services at scale is part of the DLP value proposition for latency-sensitive inline enforcement (buyers should validate performance in their geographies and traffic patterns).

• Breadth of cloud app context typical of CASB heritage: Strong alignment to SaaS instance awareness and activity context, which can improve policy precision compared to network-only DLP approaches.

• Precision classification tooling (AI based file classification/fingerprinting/exact match/OCR): These mechanisms can be important in large enterprises with heavy compliance regimes, though operational overhead and efficacy should be validated rather than assumed.

• Platform integration surface (Cloud Exchange): Positioning for integration with third-party tools and workflows, relevant for SOC integration and evidence to operations pipelines.

SACR Key take away:

Netskope is a strong shortlist candidate for enterprises that are adopting (or rationalizing toward) SSE/SASE and want DLP integrated into the same real-time enforcement plane, while also maintaining API-based governance for sanctioned SaaS content at rest. It is best-fit when the organization can commit to the operational prerequisites of inline control (steering, decryption strategy, exception governance) and wants one policy approach spanning both inline and API modes.

Palo Alto Networks

Vendor Profile

Palo Alto Networks positions Enterprise DLP as an integrated component of a broader security platform, with a center of gravity in AI-powered classification, inline prevention and centralized policy administration. The product story is strongest when the buyer’s goal is to enforce consistent data protection policies wherever data moves, especially across web, SaaS, and AI-driven workflows and SaaS usage through a single control plane, rather than deploying a standalone DLP suite. Palo Alto Networks is typically evaluated in programs where organizations are already modernizing toward SSE/SASE architectures, where DLP becomes embedded directly into traffic paths and user workflows rather than bolted on post-facto.

Products/Services Overview

Palo Alto Networks’ DLP capability set is oriented around:

• AI-powered discovery and classification utilizing 1,000+ machine learning and LLM-based classifiers for structured and unstructured data

• Centralized policy definition and management for sensitive data controls

• Inline inspection and enforcement across all data-in-motion vectors including web, SaaS, email, endpoints, and on-premises networks (traffic-path enforcement)

• Multi-channel coverage that extends across data-in-motion, Data-at-rest, and data -in-use via platform integrations.

• Incident workflow and operational handling (alerting, triage, escalation, and integration into security operations processes)

• Support for common DLP actions (block, quarantine, encrypt, redact and sanitize patterns, and related preventative or corrective actions depending on channel)and end-user coaching/remediation notifications.

• Governance-oriented outcomes: auditability, reporting, and consistent application of policy intent across the enforced surfaces

Core Market Category

• Integrated DLP (delivered inside a broader platform)

Market Sub-Category

• SSE/SASE-Integrated DLP

Great DLP Reset Alignment

Inline SSE/SASE enforcement (data-in-motion)

Palo Alto Networks aligns to the Great DLP Reset primarily as a modern in-motion control point vendor where DLP is delivered as a centralized control plane enforced through the SSE/SASE inline traffic path for web, SaaS, AI-workflows, email, endpoints, and on-premises networks, rather than classic perimeter appliances or endpoint-only programs. In the storyline, PANW represents the shift to enforcing policy where data actually moves in SaaS-heavy and hybrid work environments. The tradeoff PANW embodies is high-leverage, enforceable prevention breadth in exchange for the operational realities of in-line programs (steering and decryption decisions, policy tuning, and ongoing exception management). PANW is therefore best framed as the SSE/SASE enforcement engine in a modern DLP architectures.

Core Functions and Use Cases

• Inline protection for web, SaaS, AI-workflows, email, endpoints, and on-premises networks : Detect and stop sensitive data exfiltration in real-time across all primary traffic paths including web, SaaS, email, endpoints, and on-premises networks

• Central policy orchestration: Define policy intent once and apply it consistently across enforced surfaces.

• Compliance and governance enforcement: Supports regulated data handling requirements with inspection, enforcement, and evidence trails.

• Operationalization for security teams: Integrate DLP signals into incident handling workflows and security operations processes.

Use Cases and Pain Points Addressed

1. Stopping sensitive uploads and sharing through web/SaaS

Prevent data leaks via browser-based uploads, web apps, and sanctioned SaaS usage when traffic can be routed through enforcement.

2. Securing GenAI Adoption: Visibility into GenAI app usage and blocking sensitive data transfers within AI prompts.

3. Reducing shadow sharing and unsafe collaboration patterns

Apply policy guardrails to common user behaviors (sharing externally, uploading to unsanctioned destinations, or moving sensitive files into risky contexts).

3. Protecting regulated identifiers and sensitive business data

Enforce controls for PII/PHI/PCI patterns and other sensitive data classes with consistent handling and reporting.

4. Establishing auditable controls (governance and defensibility)

Provide traceability for why something was detected or blocked and how policy was applied, useful for audit readiness and post-incident review.

5. Extending DLP into emerging AI-era workflows

Address data exposure risk created by GenAI usage patterns.

Differentiation and Competitive Novelty

• DLP as part of a broader control plane: The main differentiator is platform consolidation, DLP isn’t an isolated tool, it’s a one policy-driven enforcement layer in a larger security stack.

• Inline enforcement strength: Palo Alto Networks is typically compelling when buyers prioritize deterministic prevention in the path over purely out-of-band scanning.

• Enterprise operational fit: Strong fit for organizations that already run large-scale network/security programs and want DLP to align with existing security architecture decisions.

SACR Key take away:

Palo Alto Networks is best viewed as a high-confidence choice for organizations that want DLP to be an enforceable, centralized control embedded in their broader security platform, especially when web and SaaS traffic-path enforcement is a strategic priority. The tradeoff is that success depends on operational readiness and steering/inspection decisions, policy discipline, and sustained program ownership. For CISOs, the decision is about whether they are prepared to run DLP as a program in the traffic path, where the payoff is broad prevention leverage.

Proofpoint

Vendor Profile

Proofpoint is a cybersecurity vendor best known for protecting people and communications, with a long-standing center of gravity in email security and adjacent compliance and governance. In DLP, Proofpoint positions its capabilities around reducing data loss driven by employee behavior, compromised accounts, and misdirected communication, with coverage spanning email and expanding into cloud/SaaS and endpoint-oriented controls as part of a broader people-centric data security and governance narrative.

Products/Services Overview

• Enterprise Data Loss Prevention (Enterprise DLP): A multi-channel DLP capability intended to apply common detectors/classifiers across channels and support unified alerting/investigation workflows.

• Email Data Loss Prevention (Email DLP / Adaptive Email DLP): Outbound email-focused DLP controls positioned to detect and prevent sensitive data leakage in email bodies and attachments, Proofpoint also emphasizes behavior and relationship context for misdirected email risk in its narrative material.

• Endpoint Data Loss Prevention: Endpoint-focused monitoring and prevention for risky file activity (Proofpoint frames Endpoint DLP as a subset of its Insider Threat Management capability set).

• Insider Threat Management (ITM): Insider-risk oriented visibility, context, and analysis that is positioned to accelerate investigation and response for user-driven data loss scenarios.

• Web Security (with DLP-adjacent controls): Proofpoint describes web protection and integrated DLP use cases such as controlling uploads to personal webmail and unapproved SaaS and using browser isolation to constrain risky interactions (copy/paste, uploads/downloads) depending on configuration.

• Data Security Posture Management (DSPM): Proofpoint markets DSPM-style discovery and classification and remediation controls as part of a broader data security portfolio which includes AI Autonomous Classifiers and agentless scanning.

Market Category

Enterprise DLP

Market Sub-Category

Email-security-integrated

Great DLP Reset Alignment

Proofpoint aligns to the DLP reset storyline primarily as an email-heritage vendor expanding DLP into a broader, multi-channel program anchored on people and communications with a narrative emphasizing reducing analyst burden by correlating content detections with user and behavioral context and investigation workflows while extending enforcement beyond email into cloud and endpoint coverage as needed. The architectural tradeoff for many buyers is that Proofpoint’s strengths often show up fastest where email and user communications risk is central (and where Proofpoint is already deployed).

Core Functions and Use Cases

• Email exfiltration prevention and mis-send risk reduction: Reduce sensitive data leakage through outbound email, including attachments and addressed recipients.

• Insider-driven data loss detection and investigation: Add user and activity context to data-loss events to help triage negligent vs. malicious vs. compromised-user scenarios.

• Multi-channel policy extension (where deployed): Apply consistent classifiers and detectors across multiple channels (email plus cloud and endpoint) to reduce one-off policy silos.

• Compliance-driven monitoring and evidence: Support monitoring and investigative evidence needed for regulated data movement.

Use Cases and Pain Points Addressed

1. Prevent sensitive data from leaving via outbound email: Enabled by email content inspection and policy-based enforcement which matters because email remains a high-frequency exfiltration path and a common source of accidental exposure.

2. Reduce misdirected-email incidents: Enabled by behavioral and relationship-oriented signals (as described in Proofpoint’s adaptive narrative) which matters because misaddressed messages are a frequent, hard to train-away failure mode.

3. Consolidate alerting and investigations across channels: Enabled by unified alert and investigation interface and reusable detectors which matters because cross-channel incidents otherwise require multiple consoles and manual correlation.

4. Endpoint file-activity prevention for everyday users: Enabled by endpoint DLP controls positioned to detect and prevent risky file activity which matters because endpoint actions (copying, staging, syncing) often precede broader exfiltration.

5. Constrain risky web interactions (select use cases): Enabled by web security plus isolation patterns which matters for controlling uploads and downloads and limiting data movement to personal webmail or unapproved SaaS in some operating models.

Differentiation and Competitive Novelty

• Email-security-led DLP integration: Proofpoint’s DLP is commonly evaluated in conjunction with its email security footprint which can reduce deployment friction for outbound email controls compared to new stack DLP rollouts.

• People/behavior context as a first-class narrative: Proofpoint emphasizes correlating content findings with behavior and threat context to speed triage and clarify intent (different from purely content-centric DLP operating models).

• Detector reuse across channels: Proofpoint provides a library of detectors and classifiers that can be applied across channels, aiming to reduce per-channel policy rebuild effort.

SACR Key take away:

Proofpoint is a strong shortlist candidate when outbound email risk, misdirected communication, and people-driven data loss are central to the threat model, especially for organizations already standardized on Proofpoint for email security and looking to extend DLP with consistent detectors and more contextual investigations. For CISOs modernizing DLP, the goal is to evaluate coverage across the exact channels that matter in your environment (priority SaaS apps, endpoints, web, and any AI-related workflows) and confirm the operational model for triage and remediation across teams.

DLP Vendor Market Map

This section provides a broader landscape view of DLP vendors beyond the profiled set, organized by the market-evolution phases described earlier. The goal is not to force a single winner list, but to show where different products typically anchor (classic suite enforcement, inline and SSE control points, discovery-led control planes, or AI-era and runtime controls) so buyers can quickly shortlist options that match their environment, deployment constraints, and maturity stage. Vendor placement is directional: many offerings span phases, but most have a primary center of gravity based on how they deliver first value and where they require the most operational investment.

Note: SACR vendor writeups below are intentionally directional. Where SACR has first-party briefing notes or public engineering and deployment documentation, the description is firmer. Where some sources are primarily marketing, wording and positions are conditional.

Practical Recommendations for CISO’s and Practitioners

Buyer Starting Point Decision Tree

DLP Reset Program Focus Recommendations by phase

• Phase 0 (classic): Retain for regulated identifiers and mature endpoint and network needs, but constrain scope and staffing expectations, refresh and integrate to advance to later phases.

• Phase 1 (SaaS and cloud sprawl): Prioritize SaaS-first enforcement and remediation in the noisiest collaboration channels.

• Phase 2 (control plane): Invest in discovery and classification and the addition of identity and entitlement context, then focus on automated remediation and workflows where possible, or human in loop for exception handling or deeper triage and remediation validation (if needed).

• Phase 3 (AI-era): Implement explicit prompt and agent controls and browser and session last-mile controls with strong logging and ideally mapping data lineage from end (source) to end (destination).

Actionable Security Program Steps for CISOs and Security Leaders

1. Establish the truth layer first (discovery + classification)

   • Implementation considerations: prioritize highest-risk SaaS and cloud data stores, define sensitivity taxonomy.

   • Success metrics: % of sensitive data discovered and classified, time-to-first-signal.

   • Timeline: Weeks for initial coverage; ongoing for expansion.

2. Map your enforcement surfaces to real runtime environments

   • Implementation considerations: Decide where API actions suffice vs where inline SSE or endpoint is necessary.

   • Success metrics: % of high-risk channels covered by enforceable controls.

   • Timeline: 1–2 quarters depending on steering and endpoint roll out.

3. Reduce policy burden with context-rich decisions

   • Implementation considerations: Integrate identity, entitlements, sharing posture, and behavioral signals to manage insider risk and reduce false positives in triage and policy.

   • Success metrics: False positive rate, analyst time per incident.

   • Timeline: Incremental, measurable within 30–60 days post integration.

4. Prioritize automated remediation and prevention over alerting

   • Implementation considerations: start with reversible actions (warn and revoke link), then escalate to redact,delete,quarantine.

   • Success metrics:

     1. Mean time to remediate (MTTR) and mean time to prevention

     2. % incidents auto-remediated

     3. Business disruption rate

     4. Timeline: 60–120 days to mature workflows.

5. Make AI workflow governance explicit

   • Implementation considerations: define controls for prompt pasting, Copilot scopes, and agent tool-call logging.

   • Success metrics:

     1. % GenAI apps covered

     2. Number of policy-enforced AI events

     3. Audit completeness

     4. Timeline: 30–90 days for initial controls depending on surfaces.

6. Treat browser and session controls as last-mile DLP where needed

   • Implementation considerations: Use targeted deployment for high-risk groups and workflows.

   • Success metrics: reduction in SaaS screenshot, copy, download leakage events.

   • Timeline: pilot in weeks; expand by cohort.

7. Align the program to measurable outcomes (e.g., compressing MTTR and anchoring the program in quantifiable risk reduction outcomes)

   • Implementation considerations: Define risk scenarios (exposed data in SaaS, oversharing to copilots, public repo leaks).

   • Success metrics: Reduction lower mean time to resolution (MTTR) in exposed sensitive objects, fewer critical exposures, improved evidence trails. (e.g., reduce MTTR, align to measurable outcomes)

   • Timeline: Ongoing, baseline within a quarter.

Security Engineering and Architectural Practitioner Guidance

First: Establish the truth layer, then prove remediation in one high-noise channel.

Start by building a defensible inventory of sensitive data (discovery + classification), then pick a single collaboration channel where risk is visible and operational friction is high (e.g., Drive and SharePoint, Slack and Teams, GitHub) and close the loop with a small set of reversible, automated actions.

• Scope: 1–3 repositories and apps with the most sensitive data and the most frequent oversharing and exposure patterns.

• Controls to emphasize: Classification and evidence trails first; automation second (revoke external links, quarantine, label, owner notification).

• Success criteria: Faster time-to-first-signal, measurable reduction in exposed sensitive objects, and a remediation workflow that doesn’t become a ticket factory.

• Avoid: Starting with broad block policies or multi-channel rollouts before you have trusted classification and stable workflows.

Next: Enrich findings with identity and entitlements context and expand repository coverage.

Once you can find sensitive data and take consistent action in one channel, add the context required to prioritize what truly matters (who has access, how it was shared, what level of exposure exists) and broaden coverage to the next set of repositories.

• Add context: identity signals (SSO and IdP), group membership, entitlement and access graphs, sharing posture, and (where possible) lineage and audit context.

• Expand systematically: Add the next repositories based on risk scenarios (customer data stores, executive collaboration spaces, developer ecosystems).

• Improve triage: Drive down false positives and analyst time-per-incident by prioritizing high impact and high exposure findings.

• Standardize policy intent: Keep policies consistent as coverage grows (same sensitivity taxonomy, same response ladder).

Then: introduce heavier enforcement points only where the risk scenario demands it (inline SSE and endpoint).

Inline and endpoint controls are powerful, but they introduce architectural and operational tax. Add them after discovery-led visibility and remediation are working, and only for scenarios where out-of-band and API actions are insufficient.

• Use inline SSE when: real-time control is mandatory (regulated egress), you need web and SaaS traffic inspection, or you must prevent exfil in-session.

• Use endpoint controls when: device-level actions are the dominant exposure path (removable media, local copies, un-managed sync clients, print and screen capture).

• Pilot with a narrow cohort: high-risk teams and users, a small set of apps, and a clear warn and block escalation path.

• Keep the program measurable: track tuning effort, user friction, and risk reduction so enforcement doesn’t recreate the classic DLP burden.

Future View: Emerging DLP Vendors and Trends

This section shifts focus to the emerging vendors and new architectural narratives that are shaping the next generation of Data Loss Prevention (DLP). It moves beyond established players to examine startups and specialized platforms that prioritize AI-driven intelligence, rapid time-to-value, and addressing specific high-friction challenges like insider risk and SaaS collaboration leakage. These companies represent the cutting edge of the DLP Reset, often employing agentless models, edge AI, and deeply integrated remediation workflows to deliver measurable risk reduction with minimal operational burden, signaling the future direction of data security control planes.

The Great DLP Reset AI and Agentic Transformation

Normalized Features Across DLP Vendors (Total of 42 vendors Assessed)

The following chart depicts the features across the in-scope Data Loss Prevention providers (total of 42) in SACR exploration of DLP vendors in the market in 2026. The features emphasize compliance oriented mandates such as providing evidence and auditability, classification of data, policy and orchestration features with the fourth priority being automation and remediation.

Below is a list of key differentiating features SACR found across data loss prevention vendors. The lower end of the features are either emerging capabilities (e.g. net new features) or features not prioritized by all vendors in the market. Features including operations oriented features such as endpoint rollout capabilities, compliance focused features such as templates and compliance packs and ability to perform revoke of sharing links and the ability to perform redact and delete. Emerging areas of stronger client interest for example in AI-era features such as prompt redaction and LLM semantic extraction from content are aligned to our called out AI-era focus. Also noteworthy is JIT coaching (just in time coaching) for guiding users in real-time to tell them how they ought to handle data and auto-tuning, also aligned to our view of future features in the data loss prevention (DLP) market.

AI-era DLP is Causing Changes Across Several Key Areas

DLP is critical given the massive expansion of sensitive data across SaaS platforms, cloud, AI, AI agents and premises environments, and into centralized repositories like data lakes, all of which present new and complex vectors for data loss. AI is increasingly being used in DLP solutions and being added to the complex architectures used, while also enabling new capabilities in emerging DLP solution features.

Below is a chart of new emerging classification technologies and their penetration and use by various vendors in our DLP Reset market analysis. Notice that technologies like LLM-based classification and ML semantics have emerged but are lower than traditional rules/regex/pattern style classifiers. This is because these are emerging technologies and capabilities in the latest generation of AI-era tools. We also noted that image recognition, OCR (Optical Character Recognition) and PDF examination was another area of variance between vendors that is often unsupported in some vendors and enforcement channels.

AI-era DLP is evolving in several key areas:

• LLM/semantic classification as the new detection engine: Higher-fidelity classification of unstructured data (docs, chat, code) and fewer brittle regex-only policies.

• Truth-layer + context becomes mandatory: Discovery-led inventories (DSPM-style) plus identity, entitlements, and sharing posture help AI and agents decide and take action on what is materially risky.

• GenAI prompt and output controls (narrow but urgent): Controls for prompt pasting and uploads, output redaction and masking, and policy-driven warnings and blocks in AI chat and copilots.

• AI and Agent governance expands the DLP surface area: Tool-call logging via MCP and platform integrations with Co-Worker and agentic platforms, least-privilege data access for agents, and guardrails that constrain what agents can retrieve/send.

• Browser and session becomes a first-class enforcement plane: Last-mile controls over copy/paste, upload and download, printing, and screen capture across SaaS and GenAI workflows.

• Shift from alerting to automated remediation: Revoke links, quarantine, redact/delete, label, and other actions that reduce exposure without creating ticket-factory operations.

• Audit-grade evidence as a competitive wedge: Investigation-ready timelines (actor, object, action, timestamp, remediation proof) and lineage and provenance where feasible.

Market bifurcation in the near term:

• Guardrail point solutions (prompt, agent and runtime visibility and control) with high relevance but narrower breadth.

• Broader DLP and control-plane platforms claiming agentic autonomy and cross-plane orchestration have high upside, but require proof of real enforcement depth and measurable burden reduction.

Longer term expected convergence towards Unified Agentic Defense Platforms ( see SACR UADP report)

Emerging DLP Vendors to Watch

This section shifts focus to the emerging vendors and specialized platforms that are shaping the next generation of Data Loss Prevention (DLP). These startups prioritize AI-driven intelligence, rapid time-to-first-signal, and addressing high-friction challenges like insider risk and SaaS collaboration and data leakage. By employing agentless models, edge AI, and deeply integrated remediation workflows, these companies deliver measurable risk reduction with minimal operational burden, signaling a future direction of Unified Data Control Planes (UDCP) and control of data (as well as other cybersecurity functions) in the browser or on endpoints.

Above Vendor Profile

Above Security (Above) positions itself as an AI-native managed insider-risk platform designed to interpret the reconstruction of user intent by correlating signals across identity, endpoint, SaaS, and AI runtimes. The platform aims to reduce operational burden by providing investigation-ready behavioral timelines and delivering coaching-first interventions to shape employee behavior in real time.

Products/Services Overview

• Investigator Fleet: Above markets a fleet of specialized investigator agents, including Shadow AI & IT, Data Exfiltration, Flight Risk, and Communications investigators to maintain continuous monitoring of high-risk exfiltration paths. Operational Packaging: Above delivers its investigator fleet as a unified behavioral investigation framework rather than discrete modules, with specific capabilities like inline coaching positioned as specialized functional add-ons.

Market Category

Insider Risk / DDR

Market Sub-Category

Behavioral Intelligence / Browser-first

Great DLP Reset Alignment

Hybrid

Above functions as a hybrid control plane within the DLP Reset framework, utilizing a browser extension combined with sanctioned SaaS APIs for enforcement and telemetry. It offers additional endpoint sensors and GenAI-specific governance capabilities, with an architecture that leverages connectors for Google Workspace, Microsoft 365, Slack, and Workday, augmented by endpoint telemetry from CrowdStrike. The platform claims rapid deployment in minutes without the requirement for manual policy authoring or complex rule configuration.

Core Functions and Use Cases

• Continuous discovery and monitoring: Above establishes visibility across SaaS and browser surfaces, including clipboards, downloads, pastes, uploads, shares, OAuth abuse, Shadow SaaS/AI usage, and risky third-party grants, OAuth, and extensions, to correlate disparate events into a single, cohesive investigation.

• Audit-grade evidence: The platform produces human-readable timelines and investigation reports designed for Security, HR, and Legal teams to provide proof of intent and remediation.

• Coaching-first intervention: Implementation of polite interventions and point-of-violation nudges to guide users away from risky actions without the friction of deterministic blocking.

• Shadow AI/IT governance: Explicit focus on identifying and governing emerging AI and IT leakage paths as a core part of the data security program.

Use Cases and Pain Points Addressed

• Reduce operational burden and alert fatigue by prioritizing investigation narratives and contextual timelines over simple anomaly detection.

• Govern emerging AI/IT leakage paths by continuously monitoring and providing visibility into Shadow AI usage.

• Drive proactive behavior change through coaching-first interventions and point-of-violation nudges, reducing accidental and negligent incidents.

• Accelerate forensic investigations with audit-grade evidence, human-readable timelines, and proof of intent/remediation for Security, HR, and Legal teams.

• Achieve rapid time-to-value with deployment in “minutes” and no requirement for manual policy creation.

Differentiation and Competitive Novelty

• Investigation narrative over alerting: Above explicitly positions itself as a move away from the ticket factory anomaly detection problem toward intent-driven context.

• Agentic investigator framing: Employs purpose-built AI agents to continuously correlate signals and reconstruct forensic timelines autonomously.

• Coaching-centric posture: Prioritizes the mitigation of accidental and negligent incidents through behavior modification rather than purely malicious detection.

SACR take-away:

Above functions primarily as a behavioral intelligence and insider-risk layer, leveraging browser & endpoint-native session visibility across modern work apps and SaaS API enforcement planes to deliver rapid time-to-first-signal and comprehensive evidentiary timelines. The platform is not intended as a replacement for classic inline SSE/SASE/GW network-path controls but serves as a critical truth layer for interpreting user intent in modern work runtimes.

Bold Security

Vendor Profile

Bold Security is an emerging endpoint-centric data protection platform positioning itself as an AI-era reboot of endpoint DLP and user risk. Its center of gravity is data-in-use control at the user action layer, real-time classification and intervention on endpoints to prevent high-risk behaviors such as copying/uploading sensitive content to unsanctioned destinations and pasting sensitive information into GenAI tools.

Products/Services Overview

• Endpoint agent and on-device classification (as positioned): Continuous analysis of data sensitivity plus user and application interactions to determine risk in real time.

• User action guardrails: Coaching, warning and blocking of risky actions before data leaves the device (copy/paste, uploads, screenshots, printing, unmanaged sync, USB).

• Investigation/evidence layer (as positioned): Forensic evidence and flow mapping with data lineage.

• GenAI-era controls (as positioned): Controls for pasting into AI tools and detecting proprietary data in screenshots/images; validate which AI apps and what actions are enforceable.

Market Category

Endpoint DLP (emerging / AI-era)

Great DLP Reset Alignment

Hybrid

Bold aligns to the Great DLP Reset primarily as a last-mile enforcement approach. Rather than relying on traffic steering through inline gateways, it aims to prevent leakage at the moment of use on the endpoint and to reduce operational burden through coaching-first intervention. If its claims hold up, Bold would fit buyers who believe the dominant leakage paths are endpoint- and browser-mediated user actions (including GenAI prompt and upload behaviors). The main architectural questions are how much of a truth layer it provides or integrates with beyond endpoint telemetry and whether it can participate in a broader remediation control plane (revoke links/redact/delete in SaaS), and whether evidence and data lineage outputs are audit-grade.

Overall Viability and Execution

Execution is likely to go well when organizations want rapid endpoint-centric time-to-first-signal, prioritize prevention and coaching at user action points, and can deploy an endpoint agent broadly with acceptable privacy and performance posture. Execution is likely to be harder where buyers expect broad, cross-channel DLP coverage (SaaS/API, inline SSE/SASE, email) and deep, well-documented classifier and integration ecosystems, because available public materials provide limited confirmation of connector breadth, action depth beyond the endpoint, and evidentiary rigor.

Core Functions and Use Cases

• Endpoint data-in-use prevention: Intervene on risky user actions (copy/upload/paste) before sensitive content leaves the device.

• GenAI leakage-path controls: Reduce inadvertent disclosure via prompts/uploads to GenAI tools (validate coverage).

• User coaching to reduce negligent exposure: Nudge users away from risky behaviors to lower incident volume.

• Investigation support via evidence capture: Provide incident context/evidence suitable for security, compliance, and HR-driven investigations (validate).

Use Cases and Pain Points Addressed

1. Prevent sensitive data from being pasted into AI tools.

2. Reduce accidental exfiltration to personal cloud and unmanaged destinations.

3. Detect sensitive content in screenshots/images (OCR-oriented claim).

4. Produce investigation-ready evidence via forensic context and flow mapping.

Differentiation and Competitive Novelty

• On-device AI positioning: Emphasis on local processing and real-time, context-aware classification.

• Coaching-first and prevention: Focused on reducing incident volume and operational drag vs alert-heavy DLP programs.

• Evidence/lineage story: Positions flow mapping and data lineage as a first-class output.

SACR Key take away

Bold is best treated as an emerging endpoint DLP vendor to monitor or to evaluate in a controlled pilot when endpoint and GenAI user actions are the dominant leakage paths and when the organization wants coaching-first prevention rather than purely detection. Before procurement, validate enforceable actions by channel, classifier transparency and tuning model, evidence schema and chain-of-custody rigor, integration depth into SIEM/case management and any SaaS/API remediation capabilities, and operational burden (deployment, exceptions, performance impact) for a real enterprise environment.

Ent (Ent.ai)

Vendor Profile

Ent is an early-stage vendor that is pursuing intent-aware security oriented around user behavior and endpoint telemetry, with an emphasis on insider-risk-style visibility and intervention for modern data movement (including AI-era misuse scenarios). Ent’s center of gravity appears closer to endpoint behavioral monitoring and intervention (a form of insider risk / user activity security) than to a traditional enterprise DLP suite with coverage across email, SaaS APIs, and inline network controls.

Products/Services Overview

• Intent-aware / behavior-driven security platform

• Aims to capture user-driven telemetry at the endpoint and use that context to detect risky data handling or insider-risk patterns, intervention models are described as warn/block/guide.

• Endpoint-focused data interaction visibility, monitoring interactions between the user and the OS (Windows, Mac, Linux), various applications and websites (with browser extension) to provide context-rich signals (e.g., focus changes, screenshots, i/o changes, copy/paste type events, clicks, i/o changes, etc).

• Intervention types extend to 20+ configurable, including warn, block, redirect, obfuscate, record, disable network, with user acknowledgement flows

• Policy/decisioning approach combined with a customizable policy engine concept blending rules and ML/LLM techniques for observability, prevention and time-bound forensic enrollment.

Market Category

Insider Risk / DDR / Intent-Aware Security

Great DLP Reset alignment (Vendor)

Hybrid

Ent maps to the Great DLP Reset primarily as a future bet on richer user and action context and faster, more workflow-native interventions with awareness training, i.e., improving the signal and investigation narrative around how sensitive data is actually being handled by users in modern app workflows, including potential GenAI misuse and click-fix attack scenarios. The tradeoff is that Ent’s enforceable coverage across the broader enterprise control plane (SaaS API actions like revoking sharing, inline network controls, email controls, etc.) is still nascent. In practice, this positions Ent as potentially complementary to established DLP controls, useful if it can deliver better context and more actionable, low-noise detections, while still requiring buyers to validate where it truly enforces versus where it only observes and alerts.

Core Functions and Use Cases

• Insider-risk-style visibility into sensitive data handling

  • Detect and investigate risky user behaviors around sensitive data movement where traditional controls may lack context.

• AI-era misuse monitoring (directional)

  • Where Ent has relevance to monitoring risky user interactions that could involve GenAI tools; exact coverage for specific GenAI apps, and whether controls extend beyond endpoint observation

• Contextual intervention at the point of user action (directional)

  • Supports coach/warn/block patterns and time-bound temporary access grants with full explainability; configurable policies with reversibility and exceptions/approval workflows supported.

  • Forensic context enrichment for investigations with full-grade evidence

  • Potential value is richer timelines and event context, audit-grade chain-of-custody and export formats should be validated by prospects.

Use Cases and Pain Points Addressed

1. Reducing blind spots around user-driven data movement

   • Enabling capability: Endpoint telemetry capturing user actions (e.g., focus changes, copy/paste-like interactions) to improve context and triage, in addition to preventing incidents with interventions. Why it matters: many DLP programs struggle with high alert volume and insufficient context for decisive response.

2. Insider risk investigations with stronger event context

   • Enabling capability: Timeline reconstruction, baseline and anomaly detection, and full evidence capture around user actions. Why it matters: insider investigations often fail due to incomplete context and disputed intent; stronger evidence/auditability can shorten investigations and improve outcomes.

3. Human-in-the-loop intervention to reduce accidental leakage

   • Enabling capability: Just-in-time warnings/coaching or blocking at the moment of action. Why it matters: CISOs often need prevention that does not rely solely on punitive blocking and that supports productivity-preserving guardrails.

4. Complementing incumbent endpoint and DLP tooling

   • Enabling capability: Coexistence model with EDR/DLP stacks and clarity on integration points. Why it matters: most enterprises will not replace endpoint and DLP incumbents quickly; the adoption path typically requires additive value with minimal operational disruption.

SACR Key take away:
Ent is best treated as an emerging vendor to monitor (or to evaluate in a controlled pilot) for organizations that want stronger user-action context and intervention on endpoints, particularly for insider-risk-style scenarios and modern human, SaaS and AI tool workflows where intent and context matter as much as content inspection for accelerating forensics investigation. Shortlist Ent when your current DLP tooling produces alerts that are hard to investigate or act on, and when you believe endpoint-level context could materially reduce false positives and improve response confidence. Before buying, validate where Ent can truly enforce actions versus observe, privacy/telemetry minimization and governance, integration depth into SIEM/ticketing and key apps, auditability and evidentiary rigor, and operational burden (deployment, tuning, and exception handling) in a real enterprise environment.

Harmonic Security

Vendor Profile

Harmonic Security prioritizes the governance of workforce GenAI adoption, focusing on identifying and mitigating prompt-based exfiltration and agentic workflow risks. Its center of gravity resides in the AI runtime enforcement plane, specifically browser sessions, desktop applications, and agentic toolchains (via MCP). The platform delivers high-fidelity, real-time sensing of sensitive content and user intent, positioning Harmonic as an enablement-led control plane designed for modern AI runtimes rather than a traditional, high-burden compliance suite.

Products/Services Overview

• AI Governance & Control Platform: Establishes centralized visibility and policy orchestration for workforce AI usage across web and enterprise surfaces to ensure secure adoption.

• Harmonic Protect: A browser-extension-led enforcement point that monitors and intervenes in employee interactions with AI tools, focusing on identifying risky telemetry and providing just-in-time user guidance.

• Endpoint Agent: A lightweight sensor intended to extend the truth layer beyond the browser to desktop AI applications and thick-client scenarios; OS support and performance overhead remain key validation points.

• MCP Gateway: A locally deployed gateway designed to discover and inventory Model Context Protocol (MCP) clients and servers, capturing interaction logs and enforcing data protection at the agentic workflow layer.

• Usage Intelligence: Provides deep telemetry into adoption patterns, aimed at helping security leaders focus governance efforts and establish a definitive truth layer for AI usage.

Market Category: Top-level solution alignment.

Browser Security / Session-Layer DLP

Great DLP Reset Alignment

Hybrid

Harmonic aligns with the Great DLP Reset as a pragmatic, AI-era control layer that addresses the fragmentation of sensitive data across AI prompts and agentic toolchains. Architecturally, it spans a truth layer for usage intelligence and a distributed enforcement plane (browser, agent, and MCP Gateway). The core tradeoff is breadth: Harmonic functions as a high-relevance wedge for AI-era leakage rather than a universal legacy DLP replacement, serving as a critical complement to existing SSE/SASE/GW and DSPM controls.

Core Functions and Use Cases

• Secure AI Adoption: Establishes governance guardrails to reduce accidental sensitive data exposure as organizations scale GenAI usage.

• Prompt Protection: Detects and intervenes in real-time to prevent sensitive prompts, uploads, or outputs from leading to data loss.

• Shadow AI Governance: Identifies risky usage patterns, including the use of personal accounts versus corporate instances.

• Agentic Workflow Controls: Monitors MCP clients/servers and enforces tool-level access controls for agents that bypass traditional web proxies.

• Forensic Auditability: Produces investigation-ready logs and context for AI-related incidents to support forensic chain-of-custody requirements.

Use Cases and Pain Points Addressed

1. Mitigating Prompt Leakage: Provides in-browser monitoring to stop sensitive data entry into public AI tools where legacy DLP lacks context.

2. Agent Least-Privilege Governance: Uses MCP Gateway to restrict agentic access to enterprise data, addressing invisible data paths.

3. Just-in-Time User Coaching: Implements stop-and-think workflows to reduce accidental leakage without resorting to punitive, high-friction blocking.

4. Establishing an Adoption Baseline: Leverages usage intelligence dashboards to inform governance planning before turning on strict enforcement.

5. Securing Thick-Client AI: Extends protection beyond the browser to desktop-based AI apps and IDE-like tools via endpoint sensors.

SACR Key Takeaway:

Harmonic Security is a high-confidence shortlist candidate when the urgent data exfiltration problem is centered on AI prompts, desktop AI clients, and emerging agentic workflows where legacy DLP and SSE controls provide insufficient telemetry. It is best-fit for security teams seeking to enable AI adoption through practical governance guardrails rather than deterministic blocklists. Before procurement, validate the exact enforcement depth per surface, the rigor of audit trails, and the maturity of platform integrations into existing identity and security operations workflows.

Jazz Security

Vendor Profile

Jazz Security is an AI-native Data Loss Prevention (DLP) vendor positioning itself as a rebuild from first principles alternative to legacy, rule-heavy DLP programs. Its center of gravity is endpoint-anchored data-in-use and data-in-motion protection: capturing high-fidelity user/workflow telemetry, reconstructing narrative context around potentially risky actions, and enabling targeted, real-time interventions (as described in SACR internal materials and vendor/public statements). In practical terms, Jazz aims to reduce the operational drag of traditional DLP (false positives, brittle policies, and long investigations) by focusing on what happened and why in a workflow, not just whether a pattern matched.

Products/Services Overview

• Forensic endpoint agent (data-in-use telemetry collection)

  • An endpoint component described as collecting user actions and metadata to capture how data is handled during real workflows (e.g., copy/paste-like actions, screensharing-like flows, uploads/downloads, and other user-driven vectors).

• Investigator / investigation automation layer (context reconstruction)

  • An analysis layer described as taking endpoint telemetry and producing pre-investigated incident narratives (sequence of actions, relevant context, and why an event may be risky) to shorten time-to-triage and reduce analyst workload.

• Natural-language policy / DLP copilot concept

  • Jazz’s materials describe a policy experience where security intent can be expressed in natural language and refined conversationally, rather than maintaining large sets of brittle rules.

• Real-time enforcement / intervention actions (endpoint action layer)

  • Jazz materials describe selective interventions such as user nudges, justification prompts, and blocking of specific risky actions.

• Unmanaged device coverage via browser plugin

  • Jazz materials mention a browser plugin option for unmanaged devices.

Market Category

Endpoint DLP Suite

Great DLP Reset alignment (Vendor)

Enforcement Plane (where blocking/action happens)

Jazz aligns to the Great DLP Reset as an emerging vendor providing last-mile, user-action-centric enforcement approach: it emphasizes data protection at the moment people actually handle data (in apps, workflows, and user interfaces), rather than starting with broad discovery/classification across cloud repositories or relying primarily on network chokepoints. The reset theme here is operational: reduce the traditional DLP burden by replacing high-volume alert streams with workflow narratives and targeted interventions. The tradeoff is scope and dependency: an endpoint-first model can be powerful for data-in-use, but it must be validated for coverage gaps (data-at-rest, SaaS-native remediation, non-user-based automation) and for feasibility in environments with strict endpoint constraints.

Core Functions and Use Cases

• Data-in-use protection on endpoints

  • Controls and visibility for how users actually interact with sensitive data during daily work, including actions that many traditional controls miss or contextualize poorly.

• Insider-risk and negligent exfiltration investigations

  • Workflow reconstruction to distinguish “one-off mistakes” from suspicious patterns and reduce investigation time.

• Operational burden reduction for DLP programs

  • Fewer, higher-context incidents rather than high-volume alerting; intended to reduce tuning workload and analyst fatigue.

• Coverage for unmanaged/contractor scenarios (where supported)

  • A browser-plugin approach is described for unmanaged devices; validate efficacy and limitations versus full endpoint coverage.

• AI-era leakage at the point of use (directional)

  • Jazz materials position relevance to AI chat interactions as another UI/workflow surface.

Use Cases and Pain Points Addressed

1. Preventing accidental leakage via common user actions

   • Enabling capability: endpoint-level observation of user actions plus targeted interventions (nudge/justify/block). Why it matters: many real data losses are negligent or accidental, and blunt blocking policies can cause heavy friction.

2. Accelerating triage and reducing false positives through workflow context

   • Enabling capability: incident narratives that incorporate surrounding workflow context rather than single events. Why it matters: classic DLP often generates alerts without enough context to act, driving “alert fatigue” and abandonment.

3. Investigating suspected insider activity with clearer intent signals

   • Enabling capability: reconstruction of sequences of actions across apps/sessions to interpret intent (accidental vs. negligent vs. malicious). Why it matters: insider cases are rarely provable from a single event.

4. Extending DLP controls to unmanaged devices via browser plugin (where applicable)

   • Enabling capability: browser plugin approach described for unmanaged devices. Why it matters is that contractors and BYOD are common gaps, but heavy endpoint control is often infeasible.

5. Supporting AI-related data exposure investigations

   • Enabling capability: UI/workflow telemetry around user interactions that may include AI chat tools. Why it matters: AI introduces new copy/paste/upload leakage patterns that can evade legacy controls or overwhelm teams with low-context alerts.

Differentiation and Competitive Novelty

• UI/workflow-centric telemetry as a design anchor

  • Competitive context: contrasts with approaches that rely primarily on network inspection or SaaS API connectors; can provide richer “intent” signals but depends on endpoint deployment.

• Pre-investigated narrative model for incidents

  • Competitive context: aims to reduce manual correlation across tools and logs; differentiates on analyst experience and speed of investigation.

• Natural-language policy / copilot-style policy management

  • Competitive context: attempts to reduce rule authoring and maintenance burden; must be validated for guardrails, testing, and auditability.

• Surgical prevention philosophy

  • Competitive context: focuses on blocking only the risky step (versus broad deny policies), which may reduce business disruption if implemented well.

SACR Key take away:
Jazz Security is best suited for CISOs who believe the biggest DLP failure mode is operational, too much noise, too little context, and controls that users bypass, and who want a modern, endpoint-anchored approach that explains incidents in workflow terms and enables targeted intervention at the moment of use. Shortlist Jazz when endpoint data-in-use risk, insider/negligent behavior, and AI-era copy/paste/upload workflows are primary concerns, and when you have the organizational ability to deploy and govern an endpoint agent responsibly.

Island

Vendor Profile

Island provides policy controls via it’s enterprise browser, enterprise network and endpoint software. In DLP architectures, Island is best understood as a browser and session enforcement plane but has been broadening its capability. It delivers controls for the last-mile user actions (copy/paste, upload/download, printing, screen capture) that often bypass network-only and traditional SASE controls, especially for SaaS and GenAI usage that happens in-browser. Uniquely Island Desktop, device control and the Island service help isolate and control data across various workspace boundaries.

Products/Services Overview

• Enterprise Browser (managed Chromium-based workspace)

• Enterprise Extension (for existing consumer browsers)

• Island Desktop (Endpoint Software)

• Enterprise Network (Policy Control)

• Enterprise Network (Access)

• Browser security and data controls (policy, isolation patterns depending on config)

• Visibility and audit logging for web and SaaS sessions

Market Category

Browser Security / Session-Layer DLP

Great DLP Reset Alignment

Enforcement Plane (where blocking/action happens)

Core Functions and Use Cases

• Prevent data exfiltration from SaaS via in-session controls.

• GenAI governance in the browser and on the desktop (prompt/response controls are possible depending on policy model and integrations) and agentic tools (For example, Island can mask sensitive data when an AI agent is interacting with a website).

• Control unmanaged devices and contractor access by moving the control point into the browser.

• Session evidence: log key actions for compliance and investigation.

• Last mile controls across both web and desktop apps, including cut/copy/paste, file application access, file movement, print, screenshot, and share controls.

• Governing and tracking data movement from corporate apps to non-corporate apps, such as file movements and copy-paste, including tenancy awareness (ex. corporate Gmail vs personal Gmail and AI services and chat interfaces)

Differentiation and Competitive Novelty

• Strong control over dominant leakage paths in SaaS environments.

• Does not require SSL/TLS break and inspection of traffic. This is useful when TLS decryption and traffic steering is politically difficult but in-session control is acceptable.

• Includes hundreds of data classifiers, including pattern matching, AI-based content classifiers and custom prompts (useful for tricky data controls like HIPAA, where you might have two pieces of data that are compliant on their own but become a violation when they are combined), exact data matching, and third-party sensitivity labels

SACR Key Take Away

For CISOs, Island is a high-leverage DLP enforcement point when the browser is the primary workspace (SaaS + GenAI). Use it to control last-mile actions, paired with data discovery (DSPM) and SaaS/API controls and SaaS API Protection (out-of-band CASB using APIs for a complete program. Island also is good for data lineage tracking and data protection within boundaries, especially important when delivering unique experiences in multi-user environments (for instance Banking, retail and hotel operations), which include recording and data lineage for sensitive operations.

Keep Aware

Vendor Profile

Keep Aware positions as an enterprise browser security platform that embeds controls directly into end-user browsers (via an extension-style approach) to address two problems that classic DLP and perimeter controls often struggle with: last-mile, in-browser data leakage (copy/paste, form entry, uploads, account switching, and GenAI prompt inputs), and browser-native threats such as phishing techniques and risky in-page behaviors. Its center of gravity in DLP is browser/session-layer prevention and monitoring rather than data-at-rest discovery or large-scale inline network inspection.

Products/Services Overview

• Enterprise browser security management console

  • Centralized policy management and visibility across “industry standard browsers” (vendor wording), with deployment described as quick/minimally disruptive.

• Browser Data Loss Prevention (DLP) use case coverage

  • Monitoring and control of in-browser actions associated with leakage (typing, pasting, uploads).

  • Controls intended to reduce leakage via personal-account usage inside the same browser context (e.g., switching between corporate and personal accounts).

  • Stated compatibility with Microsoft Purview sensitivity labeling/workflows.

• Browser Detection & Response (BDR) / threat prevention capabilities

  • Click-by-click telemetry and DOM analysis (vendor-described) for investigation and blocking of certain browser-resident threats and in-page behaviors.

  • Coverage claims include phishing patterns (e.g., “browser-in-the-browser”) and the ability to block actions such as copy/paste, uploads/downloads, network requests, and credential-related behaviors.

• Security operations integrations

  • Vendor claims integration into SIEM/SOAR and common workflow tools (examples shown publicly include Splunk, Microsoft Sentinel, Tines, Jira, Rapid7, Slack, Microsoft Teams).

Market Category

Browser Security / Session-Layer DLP

Market Sub-Category

Browser/session (last mile)

Great DLP Reset alignment

Enforcement Plane (where blocking/action happens)

Keep Aware aligns to the Great DLP Reset narrative by treating the browser session as a primary enforcement point for modern data movement and AI-era leakage paths. Instead of assuming a small number of network chokepoints or relying on after-the-fact SaaS API remediation, the approach emphasizes last-mile user actions,typing into web apps, pasting into chat interfaces, uploading files, and interacting with GenAI prompts, where sensitive data often leaves the organization despite existing controls. The architectural tradeoff is that value depends on consistent endpoint/browser coverage and sound policy tuning in the browser runtime; it complements (rather than replaces) data discovery/truth-layer platforms and may not address non-browser channels (thick clients, unmanaged endpoints, or backend-to-backend data flows) without additional tooling.

Core Functions and Use Cases

• Browser/session DLP for modern SaaS work

  • Reduce leakage via user actions inside web applications (copy/paste, uploads, form entry).

• GenAI prompt/input governance at the browser layer

  • Apply controls to sensitive data entered into public or unsanctioned AI web experiences.

• Personal account and identity boundary controls

  • Detect/limit risky mixing of corporate and personal accounts during browser sessions.

• Browser threat detection and investigation

  • Provide investigative telemetry (click-by-click) and browser-native detections oriented to phishing and malicious in-page behaviors.

• SOC workflow integration

  • Feed browser events into existing SIEM/SOAR and collaboration tools to reduce tool sprawl.

Use Cases and Pain Points Addressed

Preventing sensitive data paste into web apps and AI chat interfaces

• Enabling capability: monitoring and policy control over typing/pasting in the browser.

• Why it matters: addresses a common leakage path that may bypass API-only controls and occurs before data becomes a stored object.

2. Controlling uploads of sensitive files into SaaS, webmail, or shadow IT destinations

   • Enabling capability: browser-layer inspection/controls on uploads.

   • Why it matters: reduces exfiltration through sanctioned browsers even when network chokepoints are inconsistent.

3. Reducing corporate-to-personal account mixing (e.g., personal webmail/storage usage)

   • Enabling capability: policy to limit/monitor access to personal accounts on corporate devices.

   • Why it matters: a frequent root cause of accidental data movement outside governed tenants.

4. Faster phishing investigation with browser-native telemetry

   • Enabling capability: click-by-click telemetry and DOM analysis to investigate what the user saw/did.

   • Why it matters: shortens time-to-triage when network and endpoint logs are insufficient to reconstruct browser UI deception.

5. Operationalizing browser signals in existing SOC tooling

   • Enabling capability: SIEM/SOAR/workflow integrations.

   • Why it matters: improves adoption when events become actionable where analysts already work.

Differentiation and Competitive Novelty

• Focus on browser-native enforcement rather than network-inline inspection

  • Competitive context: positioned against “proxy + decryption” approaches; promises value where inline steering is undesirable or infeasible.

• Inside-the-browser visibility claims (DOM analysis, user-action telemetry)

  • Competitive context: differentiates from tools that only see URL/category or network flow metadata.

• Combination of DLP-style controls and detection/response framing

  • Competitive context: attempts to unify data leakage controls with phishing/threat investigation in a single browser-resident control point.

• Purview adjacency (sensitivity labels) and SOC tool integrations

  • Competitive context: tries to coexist with incumbent suites rather than forcing rip/replace.

SACR Key take away:

Keep Aware is best evaluated as a browser/session enforcement layer for modern DLP, particularly for SaaS-heavy organizations struggling with copy/paste, uploads, account switching, and GenAI prompt exposure that bypass traditional chokepoints. Shortlist it when the security program wants practical, last-mile controls without committing to broad inline steering/decryption changes, and when phishing investigation would materially benefit from richer browser-native evidence.

MIND (mind.io)

Vendor Profile

MIND is an AI-native data security platform focused on modernizing data loss prevention and insider risk by combining discovery and classification of sensitive (primarily unstructured) data with runtime prevention controls across endpoints, browsers, SaaS apps, email, Agentic AI and GenAI usage. Its center of gravity is autonomous and low-ops DLP that aims to reduce the policy and triage burden through richer classification, context, and automation, while still providing concrete enforcement at user-controlled leak points (endpoint + browser) and remediation for exposure in common SaaS repositories.

Products/Services Overview

• Data discovery and classification (“MIND AI” / multi-layer classification)

  • Continuous inventory and classification of sensitive data across connected environments (SaaS, endpoints, on-prem file shares, email), oriented to unstructured content.

  • Multi-method approach: combines deterministic techniques with proprietary SLM/LLMs and statistical/semantic approaches to improve precision and reduce false positives.

• Data detection and response (DDR) / context-driven investigation support

  • Enrichment of risky events with who, what, where, when and why context, with prioritization intended to reduce analyst noise.

  • Redacted meta-data of source file is available (e.g., storing redacted representations rather than full sensitive values).

• Loss prevention and mitigation (runtime controls and remediation)

  • Endpoint agent + browser extension used for prevention at most common exfiltration paths (copy/paste, uploads, local actions), and for capturing lineage/evidence.

  • Automated/assisted remediation actions: revoke access/remove public links, quarantine/delete, apply labels, engage data owners, etc.

  • Block with override/justification and user coaching/notifications are supported as a way to reduce friction while still enforcing policy intent.

• Integrations and ecosystem connectivity

  • Public materials reference integration with identity (notably Okta) and common enterprise/SaaS systems; integrations with collaboration tools (Slack/Teams) for nudges/coaching and workflows are also supported.

• Trust/compliance posture (public trust center)

  • The public trust center lists SOC 2 Type 2 and ISO/IEC 27001:2022, plus ISO/IEC 42001:2023 (AI management system), and regulatory frameworks (e.g., GDPR, HIPAA, CCPA).

Market Category

Insider Risk / DDR / DLP / DSPM

Great DLP Reset alignment (Vendor)

Hybrid

MIND aligns with the Great DLP Reset shift by treating DLP less as a set of brittle, channel-specific regex rules and more as a continuous data security program anchored in better classification and context, then applied across distributed enforcement points. Architecturally it behaves like a hybrid of: truth-layer discovery/classification for unstructured data, a control-plane experience to prioritize issues and drive remediation, and enforcement at the user action layer (endpoint and browser) for AI-era leak paths (prompt pastes, uploads, local exfil). The tradeoff is that this approach depends on endpoint/browser rollout and integration depth; it is not inherently an inline proxy/SSE choke-point model, and it may not address all agent-to-agent or backend-to-backend data flows without additional controls.

Overall Viability and Execution

Publicly, MIND has communicated a $30M Series A (June 2025) and positioning as a fast-moving vendor in autonomous DLP. The company also states recognition as a RSAC 2025 Innovation Sandbox finalist (Top 10), Blackhat Startup Spotlight Honorable Mention (2025). These are signals of momentum, but they do not fully substitute for operational diligence on support maturity, roadmap execution, and referenceability.

What tends to go well with this type of platform is rapid visibility into unstructured data risk plus pragmatic controls at common leak points (endpoint and browser). What tends to be hard is scaling endpoint and browser deployments, aligning security controls with business workflows (especially where blocking is politically costly), and ensuring integrations deliver real remediation and audit-grade evidence rather than simply producing more alerts.

Core Functions and Use Cases

• Unstructured data discovery and classification across modern estates

  • Find and classify sensitive content in files and common repositories to establish a credible baseline for policy decisions.

• Insider risk detection with context and prioritization

  • Identify risky behaviors and reduce false positives by using identity/activity/destination context.

• GenAI usage guardrails for common user-driven leakage paths

  • Detect and prevent sensitive data from being pasted or uploaded into GenAI tools (browser-mediated) and similar destinations.

• Endpoint-centric enforcement and evidence collection

  • Control data-in-use actions (e.g., USB/peripherals, local actions) and collect investigation artifacts (activity logs; screenshots are referenced in internal notes).

• Automated remediation for exposure in SaaS and collaboration environments

  • Reduce dwell time for oversharing/public link exposure via targeted remediation actions (subject to connector action depth).

Use Cases and Pain Points Addressed

1. Rapidly identifying sensitive unstructured crown jewel data across SaaS and file shares

   • Enabling capability: continuous discovery + multi-layer classification across connected sources.

   • Why it matters: without credible classification, prevention controls become noisy and politically fragile.

2. Reducing oversharing risk in collaboration platforms (e.g., public links, external sharing)

   • Enabling capability: detection of exposure + remediation actions (remove links/revoke access/quarantine/delete, depending on system).

   • Why it matters: many real incidents start as “quiet” oversharing, not obvious exfiltration.

3. Preventing sensitive prompt pastes/uploads into GenAI web tools

   • Enabling capability: browser/endpoint-mediated inspection prior to destination submission (as described in internal notes).

   • Why it matters: GenAI prompts are now a frequent, high-velocity leak path that bypasses legacy controls.

4. Controlling endpoint exfiltration paths while preserving legitimate workflows

   • Enabling capability: endpoint agent controls plus policy actions like block, warn, or “block with override + justification.”

   • Why it matters: reduces operational friction by supporting exception handling without abandoning enforcement.

5. Building investigation-grade context for insider and accidental leakage

   • Enabling capability: context enrichment and evidence capture (logs/metadata; screenshots referenced in internal notes).

   • Why it matters: accelerates time-to-understand and supports auditability.

6. Agentic AI visibility:

   • Enabling capability: Endpoint and can place controls on endpoint agentic workflows.

   • Why it matters: Complementary approach to AI and agentic data loss and visibility.

Differentiation and Competitive Novelty

• Autonomous DLP posture that combines discovery, prevention, and remediation in one workflow

  • Competitive context: contrasts with single-plane tools (discovery-only DSPM, or enforcement-only DLP) that require stitching.

• Multi-layer classification approach explicitly described as beyond regex-only, with tuning intended to reduce false positives

  • Competitive context: aims to reduce alert fatigue vs legacy DLP engines; depends on real-world precision/recall under customer data.

• Endpoint and browser enforcement emphasis (plus SaaS connectors)

  • Competitive context: focuses on user action leak paths and AI-era interactions rather than relying on network-inline choke points.

• Trust posture emphasizing AI governance standards

  • Competitive context: ISO/IEC 42001:2023 (as publicly announced) is a notable signal in AI-heavy security tooling, but buyers still need to validate how models are governed operationally (change management, testing, drift, explainability).

SACR Key take away:

MIND is best considered by CISOs who want a pragmatic, AI-era modernization of DLP and insider risk that reduces operational drag by pairing stronger unstructured-data classification with enforcement at the user action layer (endpoint + browser) and automated remediation for common SaaS exposures. Shortlist it when the organization’s dominant leakage paths are browser/endpoint-mediated (including GenAI prompt and upload behaviors) and when the team needs faster time-to-value than multi-quarter classic DLP rollouts.

Orion Security

Vendor Profile

Orion Security is an AI-native data loss prevention platform positioned to reduce the operational brittleness of traditional DLP by shifting emphasis from static, manually maintained policies to context-rich detection of why sensitive data is moving. Its center of gravity is real-time detection and prevention of risky data movement across multiple enforcement points (endpoint agent, browser extension,email gateway, and SaaS/API integrations), with particular focus on high-noise environments where security teams struggle to distinguish legitimate workflow activity from true exfiltration, especially as GenAI usage increases the volume of sensitive data interactions.

Products/Services Overview

• Unified DLP platform spanning multiple enforcement surfaces

  • Endpoint agent capabilities intended to observe and control local data actions and common exfiltration paths.

  • Browser extension capabilities intended to control last-mile user actions in web/SaaS and GenAI interactions.

  • Email Gateway that adds another security layer to prevent data exfiltration though emails, even when sent through unmanaged devices (like mobile phones).

  • SaaS/API integrations intended to observe and act on risky data movement and oversharing in connected services (action depth varies by connector and must be validated).

• Context- and behavior-oriented detection engine (“beyond policies” positioning)

  • Uses context signals (identity, destination, environment, and lineage) to judge likelihood of data loss vs normal business activity.

• Data lineage / tracing-oriented investigation support

  • Emphasis on mapping or reconstructing data movement paths to improve triage and provide investigation narrative (what happened and how data moved).

• User interaction and enforcement workflows

  • Vendor materials describe controls such as warnings/coaching, justification/override patterns, and automated blocking, with escalation options.

• Integration into security operations workflows

  • Vendor-provided materials describe integrating into existing SOC/case management patterns.

Market Category

Enterprise DLP Suite

Great DLP Reset alignment

Hybrid

Orion aligns with the Great DLP Reset thesis by treating DLP as a context-driven control plane paired with distributed enforcement, rather than a policy spreadsheet attached to a few chokepoints. The model is oriented to using richer context to reduce false positives and avoid policy sprawl, placing controls closer to where modern data movement occurs (endpoint and browser-mediated SaaS and GenAI workflows), and providing a more investigation-ready view of data movement (lineage/flow framing). The primary tradeoffs are typical of emerging AI-native DLP approaches: buyers must validate explainability and governance of AI-driven decisions, confirm the practical breadth of integrations and enforcement depth, and ensure deployment/change-management (agents and extensions) is acceptable at scale.

Overall Viability and Execution

Public reporting indicates Orion raised a significant funding round in early 2026, and investor commentary portrays strong early go-to-market momentum. This supports near-term viability and suggests the company is investing aggressively in product development and enterprise sales, but it does not substitute for customer diligence on support maturity, roadmap stability, and implementation outcomes across diverse environments.

Orion is described as highly responsive during RFP/POC phases, with a set-and-forget aspiration (reduced tuning burden versus legacy DLP). What tends to go well are deployments, fast initial visibility and a clearer signal-to-noise story when buyers are already suffering from alert fatigue. What tends to be hard: enterprise-scale rollout (endpoint + browser), providing consistent enforcement across varied data channels, and meeting the expectations of organizations that require mature global support, deep compliance artifacts, and highly deterministic controls for regulated workflows.

Core Functions and Use Cases

• Reducing false positives and policy sprawl in DLP programs

  • Use context/behavior to improve precision and reduce ongoing tuning overhead.

• Securing SaaS and browser-mediated workflows (including GenAI use)

  • Control and monitor common last-mile leak paths such as web uploads, copy/paste, and prompt entry.

• Endpoint-centric prevention for data-in-use actions

  • Address local exfil paths (e.g., file movement patterns, local application interaction) with agent-based controls.

• Incident investigation and evidence-building for data movement

  • Provide lineage/flow visibility to accelerate triage and support auditability.

• Augmenting existing DLP investments rather than forcing rip-and-replace

  • Public interview content indicates Orion may run alongside incumbent stacks in larger enterprises, focusing on context enrichment and reduction of noisy detections.

Use Cases and Pain Points Addressed

1. Cutting noise in existing DLP deployments without weakening enforcement

   • Enabling capability: context-aware detection to distinguish legitimate business workflows from suspicious movement.

   • Why it matters: reduces analyst fatigue and increases trust in DLP signals.

2. Preventing sensitive data entry into public GenAI tools via browser sessions or desktop apps

   • Enabling capability: Browser extension and enforcement for copy/paste and prompt submission patterns desktop app for data exfiltration prevention.

   • Why it matters: GenAI creates a high-frequency, human-driven leak path that traditional DLP often misses or over-blocks.

3. Stopping risky uploads/shares from endpoints to SaaS destinations

   • Enabling capability: endpoint agent + SaaS context to judge destination risk and take actions (warn/block/justify).

   • Why it matters: many leaks are “normal” user workflows pointed at the wrong destination or account context.

4. Detecting anomalous exfiltration behavior (insider or compromised identity patterns)

   • Enabling capability: behavior/intent analysis combined with identity and environment signals.

   • Why it matters: material incidents often look like legitimate access until correlated with context and unusual movement.

5. Building investigation-ready narratives of how data moved

   • Enabling capability: lineage/flow mapping to show the sequence of movement and implicated users/apps.

   • Why it matters: speeds containment decisions and improves defensibility in audits and post-incident reviews.

Differentiation and Competitive Novelty

• Beyond policies positioning: intent and workflow-aware DLP rather than rule-first DLP

  • Competitive context: contrasts with legacy suites that rely heavily on static patterns and long tuning cycles.

• Multi-surface enforcement portfolio (endpoint agent, browser extension and SaaS/API)

  • Competitive context: attempts to unify prevention across the most common modern leak points without requiring a full SSE/SASE-inline architecture.

• Lineage/flow emphasis as a primary mechanism for trust and triage

  • Competitive context: aims to compete on evidence quality and analyst usability, not only detection.

• Coexistence model for large enterprises

  • Competitive context: public interview material indicates Orion may supplement entrenched platforms (e.g., to reduce noise and add context) rather than demanding immediate replacement.

SACR Key take away:

Orion Security is best suited for CISOs who are dissatisfied with legacy DLP’s tuning burden and false-positive fatigue and want a more context-driven approach to preventing real data loss, especially in SaaS-heavy environments where GenAI usage and browser-mediated workflows dominate. Shortlist Orion when you need faster, higher-trust signal and practical enforcement at endpoint and browser leak points, and when the organization is willing to evaluate an emerging vendor’s AI-driven detection model through a rigorous pilot.

Teleskope

Vendor Profile

Teleskope positions as a data protection platform that combines event-driven discovery/classification with native policy-driven remediation and preventive controls, oriented primarily around reducing data exposure and privacy and compliance risk across cloud data stores and SaaS applications. Teleskope’s center of gravity is closer to DSPM-style discovery plus DLP workflows (automated fixes inside connected systems) rather than classic in-line network DLP enforcement across all channels.

Products/Services Overview

• Connectors + discovery and scanning pipeline: Connector-based enrollment for data sources, crawling to inventory assets and scanning to classify sensitive elements and documents and collect metadata for an observatory view.

• Classification engine: Broad set of sensitive data elements and support for structured/unstructured sources overlayered by context intelligence (document type, access levels, business profile, etc).

• Policy engine (Policy Maker): Declarative policies using triggers (e.g., sensitivity, accessibility, staleness), scoping filters, and actions can run in observe-only mode before enabling remediation actions. Policy simulation is also available.

• Native automated remediation / data transformation actions: Actions such as revoking access, redaction and anonymization, masking, quarantining, relocating, deterministic encryption with referential integrity, synthetic replacement, and workflow-driven notifications and tickets are available.

• Alerting and workflow integrations: Documented integrations include Slack, Jira, Email and other third-party automation/alerting paths (buyers should validate depth e.g., bidirectional ticket updates vs one-way notifications).

• AI governance: Public materials describe controls intended to reduce sensitive data usage in training and inference and to support a redact API embedded in workflows. Specifically, Teleskope is among four OpenAI approved partners for conversation message logs in the Logs Platform meaning it offers real time sensitive data detection and remediation for AI Agent conversations.

Market Category

DSPM / Data Security Platform with DLP Workflows.

Great DLP Reset Alignment

Hybrid

Teleskope aligns to the modern DLP reset narrative as a platform that starts with event-driven discovery/classification (truth-layer mechanics) and then expresses governance through a policy/control-plane construct that can drive remediation actions in connected systems. The architectural tradeoff to validate is where prevention actually occurs: Enforcement is delivered API-driven and data-store–driven (e.g., redaction, access changes, deletion), a deliberate architectural choice prioritizing near real-time coverage at the data layer without the latency and agent-dependency constraints of in-line controls which may not replace in-line controls for web traffic or endpoint data-in-use exfiltration paths without complementary enforcement technologies.

Overall Viability and Execution

Execution tends to go well when organizations want rapid visibility into where sensitive data resides across cloud and SaaS sources, and when they have a clear plan for remediation actions that can be safely automated (with approvals/guardrails) per connector. Execution tends to be harder where customers expect a single product to provide broad, real-time prevention across every channel (web, endpoint, email, SaaS, Slack, OpenAI, GenAI prompts), because the available materials emphasize discovery plus policy-driven remediation, and the exact enforcement points and response latency vary by connector and deployment model.

Core Functions and Use Cases

• Sensitive data discovery and inventory: Building an inventory of assets and locating sensitive elements across connected stores and applications to reduce unknown exposure.

• Exposure reduction via policy automation: Identifying risky accessibility conditions (e.g., overly permissive sharing) and triggering actions/notifications through policies where supported.

• Privacy operations enablement: Supporting privacy-driven workflows such as DSAR-oriented deletion/retrieval based on classification findings and downstream integrations (validate exact system coverage and action depth).

• Data minimization and lifecycle hygiene: Using staleness and age-based signals to drive cleanup workflows (where connector metadata supports it).

• AI-era data readiness (as positioned by the vendor): Applying redaction/masking and dataset-use controls intended to reduce sensitive data propagation into AI workflows (validate which AI surfaces are actually controlled vs monitored).

Use Cases and Pain Points Addressed

1. Reduce overexposed collaboration data in SaaS repositories: Uses connectors and classification to find sensitive content and (where supported) revoke access or remediate sharing conditions, reducing the blast radius of accidental oversharing.

2. Automate redaction/anonymization for persisted sensitive data: Provides defined redaction mechanisms (masking, synthetic replacement, deterministic encryption with referential integrity) to transform data at rest as part of policy workflows or API-driven integration, useful for lowering exposure in non-production environments and certain compliance use cases (validate reversibility, key management, and downstream app compatibility).

3. Support DSAR deletion/retrieval workflows: Uses classification findings to locate subject data across systems and drive deletion/retrieval steps through integrations (validate the full set of systems where delete is supported vs locate and ticket).

4. Route findings into existing SecOps/ITSM workflows: Integrations such as Slack/Jira/Tines can turn findings into operational tickets/alerts, helping teams avoid a net-new console-only workflow (validate deduplication, ownership routing, and closure feedback loops).

5. Policy-driven monitoring for stale or risky data: Staleness triggers and connector metadata can be used to flag or act on data that is old, orphaned, or otherwise a governance concern (validate metadata quality per connector).

Differentiation and Competitive Novelty

• Context-aware classification beyond pattern matching: Unlike regex-based or flat ML classifiers that identify sensitive data by matching known patterns, Teleskope’s classification engine builds a model of the customer’s specific environment — incorporating document type, business context, access levels, and intent inference — to identify what is actually risky in that organization. This enables classification of sensitive documents that contain no regulated data fields (e.g., M&A term sheets, proprietary formulas, board-level communications) and materially reduces false positives by understanding that some data is expected to look sensitive. The engine is built on a hierarchical multi-head architecture and is complemented by Prism, a document intelligence capability that classifies documents as whole objects rather than scanning for field-level patterns within them.

• Remediation-forward posture: Compared with visibility-heavy DSPM tools, Teleskope emphasizes taking actions (redact,mask,encrypt,delete,revoke access) as first-class outcomes, not just findings (buyers should validate safety controls, approvals, and rollback patterns per action).

• Deployment flexibility (including self-hosted into customer cloud): Teleskope offers single-tenant SaaS and air-gapped self-hosted options, which may matter for regulated buyers with data residency or operational control requirements.

• Policy model based on sensitivity, accessibility and staleness: A relatively straightforward policy vocabulary can help teams operationalize governance without modeling every scenario as a separate DLP rule tree (validate how this maps to complex exception handling and business-unit workflows).

SACR Key take away:

Teleskope is best evaluated by CISOs as a discovery-to-remediation data security platform that can reduce data exposure and privacy/compliance risk across cloud and SaaS footprints,especially where the organization wants policy-driven automation (redaction, access tightening, deletion workflows) rather than only visibility. Shortlist it when your primary challenge is sensitive data sprawl and slow or manual cleanup and when you can grant the permissions needed for enforceable connector actions.

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Conclusions

DLP is being rebuilt into a new Unified Data Loss Control Plane (DLCP) because enterprise data no longer lives behind a few enforceable chokepoints, data at rest is difficult to map and share with other data security tooling, AI is leaking data through shadow AI and AI workflows have introduced entirely new leakage paths through coming agent to agent interactions. The market direction is clear: discovery-led truth layers, context-rich policy decisions, and automation-driven remediation are replacing the old model of static rules and endless tuning. Solutions are moving to runtime enforcement vs detection and response through manual processes, and autonomous operations are becoming available rapidly to enhance data defense in the era of realtime agents and greater sprawl.

SACR Key Takeaway: For CISOs, the strategic implication is to treat DLP as a control plane program rather than a single product. Winning strategies start by making sensitive data and access realities visible, then apply the minimum necessary enforcement across SaaS APIs, inline controls, endpoints, and browser and AI surfaces, with remediation automation and evidence quality as the core success measures. DLP is dead. Long live the Data Control Plane. The DLP reset favors platforms that enable a data control plane and programs that reduce time-to-value, shrink tuning burden, and credibly govern SaaS and AI workflows with automated remediation and auditable evidence.

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Sources and References

• 1 - AI adoption has reached 73% of enterprises in 2026, while real time security governance is just beginning to emerge at 7%. (Netskope)

• 2 - A commissioned study conducted by Forrester Consulting on behalf of Google, “Cloud Workers Are Key To Disruption Preparedness”, 2020.

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