Executive Overview

Cybersecurity architecture in 2026 is undergoing its most significant shift since the transition from on-premises infrastructure to the cloud. AI is no longer just an application-layer enhancement; it is becoming an autonomous execution layer within the enterprise. AI agents now read data, write code, call APIs, and execute workflows with increasing delegated authority. They are no longer just processing information; they are taking action.

Traditional security models, which assume deterministic software behavior, were not designed for systems that reason, interpret intent, generate novel outputs, and dynamically decide what to do next. Agentic AI is probabilistic and adaptive, creating a new class of risk where data is synthesized and re-contextualized, access is delegated to autonomous agents, and attacks target reasoning itself.

As AI adoption accelerates, enterprises are finding that their security stack is fragmented across identity, data, runtime, governance, and detection layers that do not communicate in real time. The industry is converging toward a new architectural model: Unified Agentic Defense Platforms (UADP). UADPs integrate data security, AI and AI agent governance, identity behavior context, runtime enforcement, and detection and response to secure AI models, agents, and the data/workflows they process.

The UADP market is actively forming and is projected to grow from $8.30B in 2025 to $21.59B in 2030, implying approximately 21.08% CAGR through 2030 (see The Rise of UADP). This growth will be driven by a shift in spending away from fragmented point solutions spanning DLP, DSPM, ITDR, NHI, and AI security toward integrated platforms that unify identity, data, and behavioral signals.

Market Definition:

Unified Agentic Defense Platforms (UADP)

Platforms that integrate a variety of core features with AI systems, data sources, and applications to unify security by providing intelligent security control, visibility, and posture assessment for AI models, AI agents, and the data and workflows they process.

What UADP Is Not

• Not just an AI security dashboard; the differentiator is policy, identity, and runtime enforcement applied to agent actions (tool calls, workflows) with audit-grade traceability.
• Not synonymous with AI Gateway, a gateway is a component; UADP governs actions and identities.
• Not ARISE (Agentic Runtime Intelligence & Security Engine), though there is overlap since UADP is the broader platform.

UADP Research Thesis

The rapid proliferation of enterprise AI agents introduces a novel attack surface, characterized by autonomous systems executing tool calls and navigating sensitive data boundaries with negligible human intervention. The Unified Agentic Defense Platforms (UADP) convergence of use cases addresses this shift by providing specialized controls designed to govern agentic workflows, treating non-human identities and tool invocations as a consolidated risk domain.

This Use Case Readiness Assessment (UCRA) indicates a nascent but developing market. Significant scaling capability is emerging in runtime enforcement and threat detection, though current offerings remain fragmented between niche specialists and established platform entities. This reflects varied architectural approaches to asserting control within the evolving AI stack.

UADP Architectural Representation

Report Scope, Objectives, and Methodology

Evaluation Group: 15 Selected Security Providers

AI agents are showing up everywhere in enterprise environments. They take actions, call tools, access sensitive data, and operate across systems with very little human oversight. That creates a new kind of security problem. Unified Agentic Defense Platforms (UADP) are the emerging category of security tools built specifically to address this: they govern AI agent behavior, enforce policy, and detect threats across the full lifecycle of AI-driven workflows.

This report evaluates how ready today’s vendors are to actually solve five practical security problems that security teams face when deploying AI agents. It is the companion to the published UADP Majestic Technoscope.

• Who’s included: Only the 15 vendors assessed in the UADP Majestic Technoscope survey are covered here. Vendors not in that cohort are excluded from scoring, regardless of market presence.
• The core question this answers: For each use case, how ready is a given vendor to deliver real value today, not in theory, not on a roadmap?
• What this is not: A feature checklist, a vendor ranking, or a forecast. Readiness tiers reflect what vendors can demonstrably do right now.
• Snapshot: 2026-Q2 (May 5th) | 15 vendors | 5 use cases: UC1 Runtime Enforcement · UC2 Data Security · UC3 AI Dev Pipeline · UC4 Threat Detection · UC5 AI Compliance

Methodology:

This report applies the SACR Use Case Readiness Assessment (UCRA) framework, a structured, evidence-first methodology that evaluates vendors against real-world practitioner use cases rather than abstract feature lists. Readiness tiers are determined through a combination of vendor-provided briefing materials, publicly available product documentation, direct analyst review, and independently verifiable customer evidence and analyst judgment. Each tier carries mandatory qualification gates: a vendor’s stated capability must be corroborated by evidence of sufficient quality before a tier is assigned or upgraded.

• Confidence levels, evidence quality ratings, and customer proof status are assessed separately and must all clear the bar for the target tier.
• Scores reflect the state of each vendor’s capability at the Q2 May 5th, 2026 research close date and do not account for developments announced or released after that point. This framework is designed to produce assessments that are fair to vendors at every stage of maturity. A lower tier reflects the current evidence baseline, not a judgment on a vendor’s trajectory or long-term potential.
• No vendor can claim a tier above Emerging without durable, verifiable artifacts, meaning evidence that can be independently corroborated by a third party and is not solely reliant on vendor self-assertion.

Qualifying artifacts include: publicly accessible product documentation or knowledge-base articles confirming a capability is generally available; named or attributable customer references that describe a production deployment; signed vendor questionnaire responses with written justification that an analyst can validate against other sources; confirmed case studies or briefing notes that include specifics (deployment context, outcome, scale); and third-party citations such as analyst firm recognition or standards-body listings. Marketing-only claims, press releases without corroborating detail, demo recordings, and roadmap commitments do not qualify. This standard applies uniformly regardless of market position, brand recognition, or survey participation level.

For a vendor to move above Emerging, the evidence bar gets progressively higher. Scaling requires confirmed general availability, a verified artifact, and large-scale deployments. Differentiated requires at least one referenceable customer, early attractive technology differentiation, and large scale. Category-Defining requires two independent references, strong differentiation, large scale, and peer analyst sign-off. These gates are enforced at peer review without exception.

The 15 Vendors in This Assessment: From SACR UADP Technoscope Ranking

Technoscope Ratings & Category Methodology Context (Only for Table Above):

• Innovators: Defined by high marks in both Delivery/Execution and vision, these entities represent the most mature platform capabilities within the current market landscape.
• Trailblazers: Providers demonstrating robust technical execution and functional delivery, though their long-term strategic vision is currently assessed at a more moderate level.
• Emerging Players: Early-stage entrants still maturing their product capabilities and market footprint, with vision and delivery scores reflecting a developing state.
• Pioneers: Entities characterized by ambitious strategic vision and a strong understanding of market direction, but with product delivery currently trailing their stated goals.

Top Use Cases Assessed in the UCRA

Detailed Overviews of Evaluated UADP Use Cases

Note: We have also conducted updates to score use cases based on the latest SACR vendor briefings (in Q1 and Q2 2026), publicly available information and vendor documentation for this assessment. Technoscope Quadrant ratings remain unchanged until the next survey and assessment.

This assessment is restricted to entities participating in the UADP Technoscope research. Assigned quadrant rankings are derived from comprehensive survey results.

UC1: AI Agent Runtime Enforcement

• Who this is for: Security architects and platform security leads whose organizations are deploying AI agents and employee-facing AI tools that touch sensitive data. They need real-time controls to prevent unsafe actions, prompt injection, and data exfiltration, without blocking legitimate work.
• What success looks like: The security team can enforce agent behavior policies in real time. They can allow or deny specific actions, limit data egress, and govern which tools an agent can call.
• The bar for Differentiated: A verified customer running in production with documented deny events and an exported policy artifact.

Vendor Use Case Analysis

As of Q2 2026, Lumia Security and Noma Security are the strongest runtime enforcement options from this survey group, both reaching Scaling. Lumia’s strength comes from its network-layer inspection; it governs traffic across more than 5,000 AI applications, including newer agentic protocols like MCP and A2A, and confirmed enforcement of ChatGPT Agent Mode with sub-950ms latency. Noma takes a lifecycle approach, covering everything from AI discovery through runtime enforcement. Both vendors have named customer references backing their claims.

Pillar and Lasso Security are at the Emerging stage. Lasso resolved a key open question in May 2026 when it confirmed that its Intent Deputy product actually intercepts agent tool calls, not just prompt/response traffic, and named Telit Cinterion as a production customer. Pillar’s inline MCP guardrail capability is confirmed via its TrueFoundry integration, but multi-agent trace and agent-to-agent enforcement still need validation. Check Point (Lakera) is also Emerging; its Agent Action Control product is confirmed generally available and does intercept tool calls, but the integration of the Lakera acquisition into the broader Check Point platform is still maturing.

Microsoft upgraded to Scaling here via the May 2026 launch of Agent 365 in its M365 E7 Frontier Suite, which discovers shadow AI agents and applies governance controls, including blocking unmanaged agents, but its enforcement depth outside the Microsoft ecosystem is unvalidated. Teleskope reached Emerging after its GenAI Gateway went generally available in April 2026, naming GoFundme, Ramp, and others as customers, though its architecture is out-of-line (not a transparent inline proxy) and agentic protocol enforcement depth remains unverified.

SACR Key Takeaway:

The open question that no vendor has yet answered with hard evidence: can any of them show a real, production-level deny event at the agent tool-call layer, not just at the prompt or response level?

UC2: Data Security for AI & Agentic Workflows

• Who this is for: Data security leads and DLP program managers whose AI systems are ingesting and processing sensitive data. They need to know what data AI is touching, and they need controls to prevent exfiltration, not the legacy DLP approach.
• What success looks like: Continuous visibility into AI data access, with adaptive controls that can actually stop exfiltration.
• The bar for Differentiated: Customer evidence of catching a real AI data exfiltration event.

Vendor Use Case Analysis

Three vendors reach Scaling here, each for different reasons. Cyera leads on AI-native data classification. Its AI Protect product has been generally available since August 2025 for public apps and M365, it acquired Ryft in April 2026 to extend into agentic AI data lake security, and it was recognized in Sensitive Data Discovery (Q2 2026) by an analyst firm with the highest strategy score. Its Agent Graph capability (launched April 2026) correlates DSPM, DLP, and identity data to map how AI agents reach and expose sensitive data. The main caveat is that enforcement beyond public apps and M365 still needs buyer validation.

Microsoft Purview reaches Scaling through sheer enterprise breadth, including the Purview AI Hub sensitivity labels at scale, and new GenAI agent protection capabilities announced in May 2026. Its depth outside the Microsoft and Copilot ecosystem is unvalidated. BigID gets there on the strength of its data discovery leadership and its newly launched Agentic Access Governance product (GA at RSAC 2026), which provides visibility and control over what AI agents can access across the enterprise data estate. BigID was also named as Leader by an analyst firm with the highest possible score in 11 criteria. The persistent gap for BigID is that it remediates after discovery; it does not block during agent execution.

Lumia Security and Noma Security are Emerging for data security. Lumia is strong on data-in-motion governance but explicitly has no data-at-rest scanning capability, a self-confirmed gap during SACR briefings. Noma’s data security module needs more depth validation before it can be considered Scaling. Other vendors at the Emerging stage include Palo Alto Networks (Cortex Cloud DSPM now generally available), SentinelOne (DSPM GA since February 2026, focused on preventing sensitive data from entering AI pipelines), and several others.

UC3: AI Development Pipeline & Model Security

• Who this is for: AppSec leads and AI/ML platform engineers whose organizations are building or fine-tuning AI models. They need protection from supply chain attacks, model poisoning, and they need security gates built into the CI/CD pipeline.
• What success looks like: Security integrated into the AI development lifecycle, including generation of AI Bills of Materials (AIBOMs) and automated policy gates in CI/CD.
• The bar for Differentiated: Customer evidence of blocking a real supply chain or injection risk in CI/CD.

Vendor Use Case Analysis

Palo Alto Networks leads this use case at Scaling with the highest confidence score (4 out of 5) in the cohort. Its Protect AI acquisition brought model scanning, AIBOM generation, and CI/CD gates under the Prisma AIRS platform. The subsequent acquisition of Portkey in May 2026 added LLM gateway depth and production AI observability. PANW’s breadth here is the strongest in the survey group.

Noma Security joins PANW at Scaling after confirming in May 2026 that it generates automated AIBOMs for every model, including dependencies, data sources, and versions. Noma also maps its outputs to OWASP, MITRE ATLAS, NIST AI RMF, and the EU AI Act, and it’s the only vendor in OWASP’s Q2 2026 AI Security Landscape listed across all nine agentic AI lifecycle stages.

BigID and Orca Security are Emerging here. BigID’s strength is at the data layer, training data governance and lineage, not at the model artifact layer. Orca’s lower delivery evidence reflects weaker execution in this dimension.

Lasso Security was upgraded to Emerging in May 2026 after confirming a Continuous AI-BOM capability that auto-inventories every homegrown AI app and agent on each code change, plus automated pipeline red teaming with 3,000+ attack payloads. The remaining gap is formal signed artifact output (the difference between running CI gates and producing a signed provenance attestation).

Lumia Security self-confirmed during SACR briefings that it has no AI supply chain or AIBOM capability, and is noted as an example of the evidence-first assessment this framework is designed to produce.

UC4: Agentic Threat Detection & Response

• Who this is for: SOC leads and detection engineers who suspect an AI agent or automated workflow is behaving badly, whether due to prompt injection, credential theft, or lateral movement between systems.
• What success looks like: The SOC can reconstruct the full chain of agent actions, identify the root cause, and contain the incident, without disrupting the broader AI deployment.
• The bar for Differentiated: Evidence of detecting a real attack or anomaly in production (not a controlled demo).

Vendor Use Case Analysis

SentinelOne is the clear leader here; the only vendor at Scaling for this use case. Its acquisition of Prompt Security brought purpose-built GenAI runtime detection claiming 99% efficacy against prompt injection at sub-30ms latency, plus MCP gateway security covering over 13,000 MCP servers. In May 2026, SentinelOne published a blog demonstrating end-to-end agent process tree forensics on a real Claude Code supply chain zero-day. The EDR traced and captured over 304 child events across a full agent execution chain, correlated back to root cause, and terminated the malicious execution while preserving the full forensic record. Its Purple AI Auto Investigation, now generally available, enables one-click agentic investigations that autonomously gather cross-stack evidence and produce complete attack timelines with closed-loop remediation. The remaining open question is cloud-hosted agent-to-agent lateral movement detection. SentinelOne has confirmed endpoint-layer forensics, but SOC-grade detection across distributed agent networks in the cloud has not been demonstrated.

Palo Alto Networks upgraded to Emerging after Prisma AIRS 3.0 (March 2026) confirmed agentic detection capabilities with a named customer (Steve Jablonski, CISO at TELUS Digital – press release), but independent validation of enterprise-scale operational maturity is still needed. Noma Security upgraded to Emerging with a confirmed UiPath named customer case study and the addition of agent-to-agent monitoring on its runtime protection platform. Pillar upgraded to Emerging after confirming multi-step agent chain detection, its platform can detect a coding agent downloading a compromised package and pushing malicious code to a production repository. Orion Security reached Emerging/Beta after confirming in May 2026 that its DLP exfiltration events feed directly into SentinelOne’s Unified Alert Management, putting it in the SOC triage queue, though behavioral threat detection beyond DLP-class monitoring still needs validation.

Several other vendors, Cyera, BigID, Orca Security, Lasso Security, and Mind Security, were also upgraded to Emerging for this use case based on confirmed SIEM integrations, runtime detection products, and behavioral monitoring capabilities documented in May 2026.

UC5, AI Compliance, Audit & Policy Governance

• Who this is for: GRC leads, CISOs, and legal/privacy officers facing regulatory requirements, EU AI Act, NIST AI RMF, SEC rules, or internal governance mandates that require documented controls, audit trails, and AI risk posture reporting.
• What success looks like: Teams can generate audit-grade evidence of AI control effectiveness and demonstrate risk posture to boards and auditors.
• The bar for Differentiated: Customer evidence of passing an audit or satisfying a regulatory inquiry using the platform’s output.

Vendor Use Case Analysis

Microsoft Purview is the strongest compliance vendor in this cohort. Its Compliance Manager includes pre-built templates for the EU AI Act, ISO 42001, and NIST AI RMF, all confirmed generally available. In May 2026, Microsoft added automated, real-time compliance assessments for AI agents via Azure AI Foundry integration, offered free for six months with Copilot or Agent licenses. The main constraint is the E5/E7 licensing requirement, organizations not already on those plans will feel the cost.

Palo Alto Networks reaches Scaling via its platform’s security posture reporting. It’s less a standalone GRC workflow tool and more a compliance story baked into Prisma AIRS. BigID also reaches Scaling: its EU AI Act and NIST AI RMF compliance modules are generally available, it has a Compliance Reporting Suite with ready-to-use audit output, and its Agentic Access Governance product (GA at RSAC 2026) provides the access control layer that underpins many compliance requirements.

Noma Security and Veeam are both Emerging. Noma has confirmed framework mapping to EU AI Act, ISO 42001, and NIST AI RMF, and generates audit-ready reports, but a board-formatted, GRC-export-ready output and actual customer audit-outcome evidence are still unvalidated. Veeam’s compliance story rests on the Securiti acquisition; the integrated Agent Commander product launched at RSAC 2026, but Veeam-integrated audit-grade workflow output has not been independently confirmed.

No vendor in this cohort has reached the Differentiated tier for AI Compliance, as no published evidence of a customer passing a real regulatory audit or inquiry using any of these platforms has been publicly documented.

Summary Heatmap

Vendor Profiles

Microsoft

Quadrant: Innovators

Best use cases: Runtime Enforcement (UC1), Data Security (UC2), AI Compliance (UC5)

Microsoft is the broadest multi-use-case vendor in this survey cohort, reaching Scaling across runtime enforcement, data security, and AI compliance as of May 2026. The May 2026 launch of Agent 365 in the M365 E7 Frontier Suite was a significant step. It discovers shadow AI agents, applies governance controls, including blocking unmanaged agents, and brings Communication Compliance to agent workflows. For compliance, Microsoft’s Compliance Manager now includes pre-built templates for the EU AI Act, ISO 42001, and NIST AI RMF, and new automated compliance assessments for AI agents via Azure AI Foundry integration were announced in May 2026, offered free for six months with Copilot or Agent licenses.

The story here is straightforward for large enterprises already deep in the Microsoft stack: it offers the widest coverage in this cohort, backed by enterprise-scale deployment experience. The constraint is equally straightforward: E5/E7 licensing is required for full capability, and enforcement depth outside the Microsoft ecosystem is unvalidated. Organizations not already on those plans, or those needing agentic runtime enforcement beyond Microsoft’s own AI stack, should evaluate separately.

Palo Alto Networks

Quadrant: Innovators (highest composite survey score)

Best use cases: AI Dev Pipeline (UC3), AI Compliance (UC5)

Palo Alto Networks earned the highest composite survey score in this cohort, reflecting its broad platform execution. Its clearest strength in this assessment is the AI development pipeline. The Protect AI acquisition brought model scanning, AIBOM generation, and CI/CD security gates under the Prisma AIRS platform, and the Portkey acquisition in May 2026 added LLM gateway depth and production AI observability. This is the strongest UC3 story in the cohort, with the highest confidence score (4 of 5).

The threat detection story improved in this snapshot, too. Prisma AIRS 3.0, launched in March 2026, confirmed agentic detection capabilities with a named customer reference from the CISO of TELUS Digital. For compliance, PANW reaches Scaling via its platform security posture reporting, though this is more a security story than a standalone GRC workflow.

Buyers already using the Prisma or Cortex platforms will find PANW a natural extension, though net-new deployments may carry higher operational complexity. The key item to pressure-test is how well the Protect AI capability is currently integrated into the Prisma AIRS platform compared to roadmap promises.

SentinelOne

Quadrant: Innovators

Best use case: Agentic Threat Detection (UC4)

SentinelOne is the detection leader in this cohort and the only vendor at Scaling for agentic threat detection. The Prompt Security acquisition brought purpose-built GenAI runtime protection with 99% efficacy claims against prompt injection at sub-30ms latency. MCP gateway security covering over 13,000 MCP servers is confirmed generally available.

The clearest evidence of SentinelOne’s capability came from a blog post published in May 2026 documenting a real Claude Code supply chain zero-day: SentinelOne’s EDR traced and captured over 304 child events across a full agent process tree, correlated everything back to the root cause, terminated the malicious execution, and preserved the full forensic record. That’s the kind of production proof this framework requires. Purple AI Auto Investigation, now generally available from RSAC 2026, adds one-click agentic investigations that autonomously gather cross-stack evidence and produce complete attack timelines with closed-loop remediation.

SentinelOne is not a fit if you need AI development pipeline security, data security governance, or AI compliance coverage; those are simply not part of its product. And the remaining open question, SOC-grade detection of cloud-hosted agent-to-agent lateral movement, is a frontier capability gap that no vendor in this cohort has answered.

Cyera

Quadrant: Innovators

Best use case: Data Security for AI (UC2)

Cyera is the strongest data security–native vendor in this cohort for AI workflows. It was named a Leader in the Forrester Wave for Sensitive Data Discovery (Q2 2026) with the highest strategy score and top marks across vision, innovation, adoption, and roadmap. Its AI Protect product has been generally available since August 2025 for public apps and the M365 environment. In April 2026, the Ryft acquisition extended Cyera’s coverage to agentic AI data lake security. The Agent Graph capability, also launched in April 2026, correlates DSPM, DLP, and identity data into a unified view of how AI agents reach and expose sensitive data.

For threat detection, Cyera resolved a key gap in May 2026: its AI Guardian Runtime Protection is confirmed for custom architectures beyond browser and M365 sessions, recording prompts, outputs, and policy events so security teams can review decisions with full context. Named customers include the CISOs of Paramount and Valvoline. For compliance, its DataPort feature assembles evidence packs for audits and regulatory inquiries.

The key limitation to validate: enforcement beyond public apps and M365. Cyera’s strength is in discovery and classification; buyers who need inline enforcement across custom agentic architectures should confirm coverage before treating it as a full enforcement control.

Lumia Security

Quadrant: Innovators

Best use case: AI Agent Runtime Enforcement (UC1)

Lumia Security is the most thoroughly documented UC1 vendor in this cohort. The Lumia Questionnaire, the most detailed vendor-provided evidence artifact submitted to this survey, confirms network-layer enforcement across more than 5,000 AI applications, including agentic protocols like MCP and A2A, with ChatGPT Agent Mode enforcement at sub-950ms latency. Gartner named Lumia a Sample Vendor in both its Market Guide for Guardian Agents and its Hype Cycle for Agentic AI in May 2026. Two named CISO references back the Scaling rating.

Lumia’s explicit self-assessment is also notable: it confirmed in writing that it has no AI supply chain or AIBOM capability. That kind of honesty is exactly what the SACR evidence-first framework is designed to surface and reward. Lumia is a complement to data security platforms, not a replacement.

The gaps are real: no data-at-rest scanning, limited lateral integration with existing DLP and DSPM tools, and the question of whether enforcement truly operates at the agent tool-call layer (versus the network proxy layer) still needs further validation. Buyers should also monitor Lumia’s 2026–2027 agentic governance roadmap execution.

Noma Security

Quadrant: Pioneers

Best use cases: Runtime Enforcement (UC1), AI Dev Pipeline (UC3)

Noma is the most complete AI security lifecycle platform among the UADP-native vendors in this cohort, covering AI discovery, posture management, red teaming, and runtime enforcement in a single platform. It earns Scaling in both runtime enforcement and AI development pipeline security.

Two evidence upgrades confirmed in May 2026 drove these ratings. First, Noma’s runtime protection page now explicitly confirms monitoring of agent-to-agent communications alongside prompts, tool calls, and MCP server interactions, closing a previous gap. Second, its AI-SPM platform page confirms automated AIBOM generation for every model, including dependencies, data sources, and versions. Noma is also the only vendor listed across all nine agentic AI lifecycle stages in OWASP’s Q2 2026 AI Security Landscape, and it was named a Representative Vendor in all five AI security categories in Gartner’s Emerging Tech Impact Radar.

Its Pioneer quadrant position reflects high ambition and strong vision scores, but lower Delivery versus the Innovators. The $200k flat-fee entry price makes this a large enterprise–first consideration. Buyers should pressure-test depth on data security (posture-only versus enforcement) and the gap between compliance framework mapping and actually producing board-ready GRC export artifacts.

BigID

Quadrant: Trailblazers

Best use cases: Data Security (UC2), AI Compliance (UC5)

BigID earns Scaling in both data security and AI compliance through its proven data discovery leadership and its new Agentic Access Governance product. The Forrester Wave named it a Leader in Sensitive Data Discovery (Q2 2026) with the highest possible score in 11 criteria. At RSAC 2026, it launched Agentic Access Governance, visibility and control over what AI agents can access and act on across the enterprise data estate, and DLP Prism, an AI-powered context-aware DLP engine. It also won two 2026 Global InfoSec Awards for AI Security market leadership.

For compliance, BigID’s EU AI Act and NIST AI RMF compliance modules are generally available, with a Compliance Reporting Suite that generates ready-to-use audit outputs and automates PIAs and DPIAs.

The persistent gap is inline runtime enforcement. BigID’s architecture is discover-and-remediate, not block-in-real-time. It identifies what AI agents are accessing and remediates after the fact; it does not intercept agent actions during execution. Buyers who need a runtime enforcement layer should evaluate BigID alongside a dedicated enforcement vendor.

Check Point (Lakera)

Quadrant: Pioneers

Best use case: Runtime Enforcement (UC1) within the Check Point ecosystem

The Check Point acquisition of Lakera in March 2026 created a UC1 candidate with a documented history: Lakera Guard has detected over 100 million real-world LLM vulnerabilities at sub-5ms latency across more than 100 languages. The named customer is Dropbox, via a pre-acquisition Lakera case study on securing GenAI innovation against prompt injection and jailbreaks. The Check Point AI Defense Plane, launched at RSA 2026, adds Agent Action Control, confirmed generally available tool-call interception, plus workforce AI security and MCP enforcement coverage.

A notable development in May 2026: Check Point’s AI Cloud Protect is now part of NVIDIA’s Enterprise AI Factory validated design for AI runtime cybersecurity, validated on NVIDIA RTX PRO Servers. This closes part of the AI Dev Pipeline (UC3) gap at the infrastructure level.

The main open question is integration maturity. Lakera was a standalone product; the Check Point AI Defense Plane is the new integration layer. Buyers should evaluate the integrated product, not the combined entity’s roadmap. Operational maturity at enterprise scale within the broader Check Point suite needs independent validation.

Pillar

Quadrant: Trailblazers

Best use cases: Runtime Enforcement (UC1), Threat Detection (UC4), AI Dev Pipeline (UC3)

Pillar has the strongest proactive AI security research output in the Trailblazer tier. Its discovery of a CVSS 10.0 critical vulnerability in n8n in February 2026, exposing hundreds of thousands of enterprise AI systems to complete takeover, is the most impactful original security research from any vendor at this market tier. Gartner named Pillar a Representative Vendor in its 2026 Market Guide for Guardian Agents.

The TrueFoundry integration (April 2026) confirmed Pillar as a first-class MCP gateway guardrail provider, inserting enforcement at the tool call level with ~10ms latency. For threat detection, Pillar confirmed multi-step agent chain detection; its platform identified a coding agent downloading a compromised package and attempting to push malicious code to a production repository. Model artifact scanning was also confirmed in May 2026 for the AI Dev Pipeline use case.

Pillar is Emerging across all five use cases, which reflects its position as a capable specialist still building out production hardening and customer validation. Multi-agent trace reconstruction and network-layer agent-to-agent lateral movement detection are unvalidated. Buyers building MCP/A2A-heavy architectures should shortlist Pillar alongside Lumia for a head-to-head enforcement comparison.

Veeam (Securiti)

Quadrant: Innovators

Best use case: AI Compliance (UC5) alongside data resilience

Veeam’s Innovator placement reflects the transformation that the $1.725 billion Securiti acquisition represents, not what the integrated product can do today. Securiti brought AI governance, data privacy management, and an LLM firewall into Veeam’s cyber resilience platform. The result, in theory, is a compelling combination: verifiable data recovery plus AI governance evidence in one platform, particularly attractive for regulated industries.

In practice, the integrated product is still early. Agent Commander, the AI risk governance product that launched at RSAC 2026, was in private preview as of March 2026 and still listed as “Request Early Access” as of the May 2026 research date. No GA date has been announced. That means all of Veeam’s UC2, UC4, and UC5 tiers remain capped as Emerging or N/A until the product actually ships. Buyers should monitor GA announcements before committing to Veeam’s AI security story.

Lasso Security

Quadrant: Emerging Players

Best use cases: Runtime Enforcement (UC1), Threat Detection (UC4)

Lasso resolved a key gap in May 2026 that had held back its UC1 rating: its Intent Deputy product confirms it actually observes and intercepts agent tool calls in the execution path, not just prompts and responses. That, combined with a named customer (Telit Cinterion, an IoT enterprise) and Gartner Cool Vendor recognition in AI Security 2024, moves Lasso to Emerging for runtime enforcement.

For threat detection, Lasso confirmed SIEM streaming, findings feed directly into your SIEM to support agent incident investigation, and multi-agent intent tracking, where the platform tracks how intent transforms across complex agent chains to detect attacks that exploit handoffs between autonomous systems. Its detection accuracy is claimed at 98.6%.

Lasso also earned a Global InfoSec Award at RSAC 2026 for Transformational AI Security & Compliance, and its compliance framework mapping (NIST AI RMF, EU AI Act, ISO 42001) is confirmed and publicly documented. The gaps: no DSPM, no identity lifecycle governance, and no AI pipeline capability, Lasso is purpose-built for agentic AI governance, and its coverage reflects that focus.

Orca Security

Quadrant: Emerging Players

Best use cases: Threat Detection (UC4), Data Security (UC2) in cloud-native environments

Orca is a cloud-native security platform layering AI detection capabilities on top of its agentless CSPM foundation. Its Runtime AI Threat Detection product went generally available in March 2026, the Orca Sensor, using eBPF-based kernel-level telemetry, captures all LLM requests and MCP activity, analyzes prompts in real time for PII, secrets, and injection attempts, and maps findings to originating workloads and identities. Its Threat Investigation Agent and AppSec Triage Agent also confirmed general availability at RSAC 2026.

Orca’s main constraint is customer evidence. Despite confirmed generally available products, no named enterprise customer was found as of May 2026 for AI-specific detection or data security scenarios. That caps its confidence scores and prevents it from reaching beyond Emerging. Its CNAPP-primary architecture means AI detection capabilities are layered additions, not the core product motion.

Orion Security

Quadrant: Emerging Players

Best use cases: Runtime Enforcement (UC1), Data Security (UC2) for employee-facing GenAI

Orion is the fastest-to-deploy vendor in this cohort, a browser extension plus sensor architecture that can be up and running in about 30 minutes. It is purpose-built for AI-native DLP: real-time blocking of sensitive data exfiltration to AI tools including ChatGPT, Claude, Gemini, Perplexity, and Copilot. Named customers are anonymous (Fortune 500 references in finance, healthcare, and technology), and the company reported $1.2 million in fraud prevented and a 96% reduction in false positives. Norwest and IBM backed a $32 million Series A in February 2026.

In May 2026, Orion resolved its threat detection gap by confirming a direct integration with SentinelOne’s Unified Alert Management, DLP exfiltration events now feed into the SOC triage queue. A March 2026 integration with Grip Security added identity context to data loss prevention signals.

Orion’s ceiling is clear: it is a DLP-primary platform for human-to-AI interactions. It has no data-at-rest scanning, no identity lifecycle governance, no AI compliance frameworks, and no AI pipeline security. The open question is whether its enforcement extends to agent tool-call chains (not just employees prompting AI tools). Buyers with employees using GenAI tools should evaluate Orion alongside Lumia Security.

Teleskope

Quadrant: Emerging Players

Best use cases: Data Security (UC2), Runtime Enforcement (UC1) for AI tools

Teleskope is the most engineering-native autonomous data security platform in the Emerging Players tier. Its classification accuracy is notably strong, achieving 97–99% F1 accuracy compared to a 60% baseline for regex-based DLP, and it claims 10% higher precision and 38% higher throughput versus competitors. In April 2026, its GenAI Gateway went generally available, making Teleskope an official ChatGPT data security vendor. Named customer wins confirmed in an April 2026 briefing and externally marketed names include Notion, Ramp, and GoFundMe.

Teleskope’s architecture is out-of-line (a pub/sub SaaS API model rather than a transparent inline proxy). This is an important distinction for buyers who need inline enforcement, Teleskope processes data out-of-band, not in the traffic path. MCP/A2A agentic protocol enforcement is also unvalidated.

One important confirmed limitation: Teleskope’s documented compliance module explicitly does not cover NIST AI RMF, ISO 42001, or EU AI Act. Its compliance coverage is for general data privacy standards (NIST 800-53, ISO 27001, SOC 2, GDPR, CPRA, PCI DSS, HIPAA). Buyers who need AI-specific compliance framework coverage should look elsewhere.

Mind Security

Quadrant: Emerging Players

Best use case: Data Security (UC2), autonomous DLP for AI workflows

Mind Security is the DLP-for-agentic-AI specialist in the Emerging Players tier. Its Autonomous DLP for Agentic AI product went generally available in January 2026, and its Autonomous DLP Analyst, which automates the full lifecycle of data security from discovery to prevention with minimal user input, went GA in March 2026. Mind covers a broad set of AI tools: ChatGPT, Claude, Copilot, Gemini, Perplexity, Glean, Cohere, and Jasper, across SaaS, on-premises, endpoints, and email.

In March 2026, Mind became the first data security company to achieve ISO 42001 certification, validating its own AI governance practices. Its integration with Okta adds identity-aware insider risk detection. The named customer on its homepage is Guild; Fortune 1000 customers are confirmed via its Series A press release.

The gaps are consistent with the platform’s focus: no identity lifecycle governance, no AI development pipeline security, and no customer-facing AI compliance framework coverage (the ISO 42001 certification is for Mind’s own practices, not a compliance product for buyers). Behavioral threat detection is a byproduct of the DLP primary motion; it is not a SOC-grade detection product.

Cross-Cutting Findings

The market is genuinely early. No vendor in this cohort has reached Differentiated or above in any use case. The tier gate, requiring at least one referenceable customer with demonstrated production outcomes, has not been met by anyone as of Q2 2026. This is not a failure of the framework or the vendor, it is an accurate picture of where the market is.

Three archetypes compete for the same buyers. The 15 vendors fall into roughly three camps:

• Purpose-built UADP specialists: Lumia, Noma, Pillar, Lasso, Check Point (Lakera).
• Broad security platform players: Microsoft, PANW, SentinelOne.
• Data and cloud security specialists extending into AI: Cyera, BigID, Orca, Veeam.

Note: Platform breadth does not necessarily equal UADP depth; buyers should evaluate use-case fit rather than brand recognition alone.

Agent identity governance is a gap in this cohort. Although the UADP technoscope did not evaluate NHI and Agentic identity, None of the 15 vendors in this survey have deep coverage of non-human identity and agent identity lifecycle governance as a primary use case. Buyers with this requirement should look outside this cohort.

Acquisition integration risk is a recurring theme. Four vendors, PANW (Protect AI), SentinelOne (Prompt Security), Veeam (Securiti), and Check Point (Lakera), carry meaningful integration maturity. Buyers should evaluate the integrated product as it exists today, not the combined entity’s stated roadmap.

The production deny-event question remains unanswered. Across all UC1 vendors, no one has published a customer-attributable production artifact showing a real qualified threat blocked, even though product claims range at the agent tool-call layer. This is the hard gate for the Differentiated tier, and it remains unsatisfied by the entire cohort.

Implications and 30/90 Day Plan for CISOs

Implications

• The market is in a high-risk, high-opportunity stage where no vendor provides truly differentiated, audit-grade proof of blocking threats at the agent tool-call layer, necessitating prioritization of verifiable Scaling execution over roadmap claims.
• Enterprises must treat the universal gap in Agent Identity Governance as an immediate architectural challenge, requiring a dedicated strategy to govern non-human identities before deploying agents at scale.

30/90 Day Plan for CISOs

1. 30-Day Focus (Discovery & Governance):

• Agent Inventory: Conduct a comprehensive discovery of all AI agents and non-human identities currently operating within the environment.
• Governance Baseline: Establish a baseline policy for agent behavior, including definitions for “allowed” versus “restricted” tool calls and data access permissions.
• Architectural Gap Analysis: Assess current security tools (DLP, IAM, DSPM) to determine if they can provide visibility into agentic workflows or if a purpose-built UADP is required.

2. 90-Day Focus (Integration & Enforcement):

• Runtime Implementation: Pilot a UADP solution to implement inline runtime enforcement on the highest-risk agentic workflows (e.g., agents with access to sensitive customer data or financial systems).
• Non-Human Identity Strategy: Integrate non-human identity governance into existing IAM workflows, ensuring every agent has a verifiable, auditable identity.
• Proactive Red Teaming: Initiate targeted red teaming exercises focused on agentic attack vectors, such as indirect prompt injection and privilege escalation, to validate the effectiveness of newly implemented controls.

SACR Key Takeaway

For CISOs, the Unified Agentic Defense Platforms (UADP) market is in a high-risk, high-opportunity stage. The primary strategic implication is that no vendor currently provides Differentiated, audit-grade proof of blocking a real threat at the agent tool-call layer. Therefore, procurement decisions must prioritize verifiable Scaling execution over roadmap claims. CISOs must treat the universal gap in Agent Identity Governance within this cohort as an immediate architectural challenge, requiring a dedicated strategy to govern non-human identities before deploying agents at scale.