EU AI Act

Tenable One AI Exposure analyzes AI posture and misconfiguration conditions to prioritize exposure reduction for AI workloads.

AuditaAI assessment: posture and exposure analysis contributes evidence for AI risk-management workflows.

AI Red Teaming runs broad and targeted adversarial campaigns to assess AI applications and agents for prompt injection, jailbreak, data leakage, unauthorized actions, and regressions.

AuditaAI assessment: broad and targeted adversarial testing contributes evidence for AI risk-management processes.

AI Model and Application Validation performs algorithmic red teaming and model vulnerability validation for AI applications and models.

AuditaAI assessment: adversarial validation workflows contribute evidence for AI risk-management activities.

Security Command Center assesses interconnected AI risks and prioritizes high-risk issues using posture analysis and virtual red teaming.

AuditaAI assessment: posture analysis and risk prioritization workflows contribute evidence for maintaining a risk-management system.

Lasso AI Security Posture Management evaluates misconfigurations and policy gaps, including supply-chain-oriented risk indicators before production rollout.

AuditaAI assessment: posture assessment and pre-production risk indicators contribute evidence for maintaining AI risk-management processes.

Mindgard platform workflows include risk analysis and remediation guidance to prioritize treatment of AI security findings.

AuditaAI assessment: documented risk analysis and remediation guidance contribute evidence for AI risk-management system maintenance.

Neo Security Platform analyzes security gaps across agentic and non-agentic software to surface exposure conditions that require remediation.

AuditaAI assessment: analysis of exposure conditions and remediation prioritization contributes evidence for AI risk-management processes.

Prisma AIRS provides posture visibility and policy management for AI assets, training and inference data, application integrity, and model access.

AuditaAI assessment: posture visibility and policy management controls contribute evidence for maintaining an operational risk-management system.

Zenity posture workflows evaluate configuration and permission risk for agents before and during deployment.

AuditaAI assessment: posture and permission-risk evaluation contributes evidence for AI risk-management processes.

Zscaler AI Security runs configurable attack simulations to identify AI vulnerabilities and provide remediation guidance before and during deployment.

AuditaAI assessment: pre- and in-deployment attack simulation contributes evidence for maintaining AI risk-management processes.

Cato AI Security applies inline controls to block sensitive data exfiltration to unauthorized AI services and channels.

AuditaAI assessment: controls over sensitive-data exfiltration in AI interactions contribute data-governance safeguards.

Sensitive Data Protection classifies and de-identifies sensitive content used for model training, tuning, and generative AI prompts and responses.

AuditaAI assessment: classifying and de-identifying training and prompt/response data contributes to data-governance controls.

Zscaler Data Security discovers and classifies sensitive data and uses contextual classification to identify data risk across GenAI and other channels.

AuditaAI assessment: sensitive-data discovery and contextual classification contribute data-governance controls for AI usage channels.

Tenable One AI Exposure provides continuous discovery of AI assets and attack-surface context across software and infrastructure environments.

AuditaAI assessment: continuous discovery and context records contribute traceability and record-keeping support.

Chainguard states that library artifacts are built from source with full provenance and signed SBOMs.

AuditaAI assessment: signed SBOM and provenance outputs contribute technical record-keeping and traceability context.

AI Agent Security discovers agents and assesses their tool and MCP access surface for security risk.

AuditaAI assessment: agent discovery and access-surface assessment contribute traceability and record-keeping context.

AI Cloud Visibility provides discovery and inventory context for AI workloads, models, data, and users.

AuditaAI assessment: inventory of AI workloads, models, data, and users contributes record-keeping and traceability context.

Microsoft Purview for AI provides classification and compliance tracking artifacts that support governance review of AI information handling.

AuditaAI assessment: compliance tracking and classification artifacts contribute record-keeping and traceability context.

Okta registers AI agents as first-class identities in Universal Directory with mandatory human owner binding and lifecycle governance.

AuditaAI assessment: identity registration with owner attribution and lifecycle governance contributes record-keeping context for traceability.

OpenShell runs autonomous agents in isolated sandboxes and enforces out-of-process filesystem, network, process, inference, credential, and skill policies at binary, destination, method, and path level, with developer-approved policy updates and an audit trail of allow and deny decisions.

AuditaAI assessment: documented allow/deny audit trails and policy constraint records contribute AI-system record-keeping context.

Idira Secure AI Agents logs agent actions and communications, recording which agent performed an action and on behalf of which user, to support governance and compliance review.

AuditaAI assessment: auditable records of which agent acted and on whose behalf contribute evidence for AI-system record-keeping context.

Zenity AI Observability builds live inventory of agents, owners, permissions, and touched data across SaaS, cloud, and endpoint environments.

AuditaAI assessment: live inventory and ownership/permission visibility contribute record-keeping and traceability context.

Zscaler AI Access Graph provides real-time visibility into how AI agents use data and identities, identifies unnecessary access, and tracks data lineage across channels.

AuditaAI assessment: real-time lineage and identity-access visibility contribute record-keeping and traceability context for AI interactions.

AWS supports human approval hooks for high-consequence agent actions and behavioral monitoring for actions outside an agent's authorized scope.

AuditaAI assessment: explicit human approval gates and oversight controls contribute evidence for human oversight context in high-risk AI operation.

Okta registers AI agents as first-class identities in Universal Directory with mandatory human owner binding and lifecycle governance.

AuditaAI assessment: mandatory human ownership and lifecycle controls contribute to human-oversight boundaries for autonomous agent operation.

NemoClaw deploys supported autonomous agents from a digest-verified hardened blueprint with restrictive filesystem and process defaults, credential filtering, routed inference, state integrity checks, and operator approval for blocked network endpoints.

AuditaAI assessment: operator approval requirements for blocked destinations contribute to human-oversight decision points in autonomous-agent operation.

Idira Secure AI Agents acts as a dynamic agent identity broker that grants an agent access only for the duration of a specific task and automatically revokes permissions once the task completes.

AuditaAI assessment: task-scoped delegated access and automatic revocation constrain autonomous action authority and support human-oversight boundaries.

Aiceberg Guardian Agent provides runtime visibility and guardrail control over agentic AI decision flows.

AuditaAI assessment: runtime guardrail controls over agent decisions contribute cybersecurity and robustness safeguards.

Amazon Bedrock Guardrails includes sensitive-information policy controls to reduce protected-data leakage in prompt and response flows.

AuditaAI assessment: sensitive-information controls in prompt and response flows contribute to robustness and cybersecurity safeguards for AI operation.

Cato AI Security governance controls constrain unsafe autonomous actions and high-risk tool usage for AI agents and applications.

AuditaAI assessment: controls over unsafe autonomous actions and high-risk tool usage contribute cybersecurity safeguards.

Chainguard Factory applies SHA-pinned source inputs, isolated SLSA L3 build controls, cryptographic signing, and reproducibility checks when producing artifacts.

AuditaAI assessment: cryptographic signing, isolated builds, and reproducibility controls contribute cybersecurity safeguards.

Chainguard Libraries provides a malware-resistant dependency catalog intended to replace direct reliance on public package registries.

AuditaAI assessment: malware-resistant dependency controls contribute cybersecurity safeguards for AI software components.

AI Agent Security helps detect prompt attacks, indirect injection, and sensitive data exposure across agent prompts, responses, and tool use.

AuditaAI assessment: runtime controls for prompt attacks and sensitive-data exposure contribute cybersecurity safeguards.

Check Point AI Security documents runtime enforcement that includes protection against harmful outputs in AI interactions.

AuditaAI assessment: documented runtime protection against harmful outputs contributes robustness safeguards.

AI Runtime Protection provides runtime guardrails for prompt and response threat mitigation.

AuditaAI assessment: runtime prompt and response guardrails contribute cybersecurity and robustness safeguards.

Cisco AI Supply Chain Risk Management provides governance and security over AI models and files used by AI applications and agents.

AuditaAI assessment: governance over AI models and files contributes cybersecurity controls over model and artifact risk pathways.

Model Armor scans prompts and responses for prompt injection and jailbreak content and can return a block verdict when a violation is detected.

AuditaAI assessment: runtime inspection and blocking of prompt-based attacks contribute to cybersecurity and robustness controls.

Sensitive Data Protection classifies and de-identifies sensitive content used for model training, tuning, and generative AI prompts and responses.

AuditaAI assessment: de-identification controls for AI data flows contribute cybersecurity safeguards against sensitive-data exposure.

Lasso runtime enforcement can block prompt-injection attempts and prevent unsafe autonomous agent actions or unauthorized tool invocation.

AuditaAI assessment: runtime enforcement that blocks prompt-injection and unsafe actions contributes cybersecurity and robustness safeguards.

Bifrost centralizes MCP tool connections and enforces authentication, access-control, and governance policies for agent tool usage.

AuditaAI assessment: authentication and governance controls over agent tool access contribute cybersecurity safeguards.

Runtime guardrails are applied to block prompt injection attack paths before harmful model behavior propagates.

AuditaAI assessment: runtime guardrails that block prompt-injection paths contribute cybersecurity and robustness safeguards.

Microsoft Foundry provisions distinct Entra agent identities and uses scoped access tokens and RBAC to authorize agent calls to MCP servers and downstream tools.

AuditaAI assessment: identity-scoped token and RBAC enforcement contributes cybersecurity and robustness controls in agent tool execution paths.

Neo Security Platform is positioned to prevent agentic threats by controlling unsafe runtime action paths and over-permissioned execution conditions.

AuditaAI assessment: controls over unsafe agentic execution paths and permissions contribute cybersecurity safeguards.

Okta Agent Gateway intercepts agent-to-tool and agent-to-API calls at runtime without requiring upstream application code changes.

AuditaAI assessment: runtime governance of agent calls contributes cybersecurity and robustness controls for high-risk AI operational pathways.

NemoClaw deploys supported autonomous agents from a digest-verified hardened blueprint with restrictive filesystem and process defaults, credential filtering, routed inference, state integrity checks, and operator approval for blocked network endpoints.

AuditaAI assessment: integrity-checked deployment blueprints and restrictive execution controls contribute cybersecurity and robustness safeguards.

OpenAI Guardrails Python runs configurable checks on application inputs and outputs so failed checks can prevent an unsafe interaction from proceeding.

AuditaAI assessment: input and output validation checks that block unsafe interactions contribute cybersecurity safeguards.

Cortex AI Gateway centralizes access policy, authentication, and tool-permission controls across first-party and third-party MCP-connected agent workflows.

AuditaAI assessment: centralized control over live agent-tool access pathways contributes cybersecurity safeguards.

TrendAI combines AI Scanner pre-deployment testing with AI Guard runtime controls to detect vulnerabilities and reduce sensitive data leakage in AI applications.

AuditaAI assessment: runtime controls designed to reduce sensitive-data leakage and unsafe behavior contribute cybersecurity safeguards.

Zenity runtime boundaries can block unsafe agent actions, including risky tool usage and unauthorized task execution.

AuditaAI assessment: runtime blocking controls for unsafe actions and tool usage contribute cybersecurity safeguards.

Zscaler Data Security inspects AI usage and prompts and can block risky access or enforce prompt DLP to prevent data loss.

AuditaAI assessment: prompt DLP enforcement and risky-access blocking contribute cybersecurity safeguards against sensitive-data exposure.

SageMaker Clarify and monitoring workflows provide ongoing model and data behavior analysis for drift, quality, and governance evidence.

AuditaAI assessment: continuous model and data behavior monitoring contributes evidence for post-market monitoring activities.

Cato AI Security runtime monitoring detects prompt-injection patterns and policy-violating AI prompt activity.

AuditaAI assessment: runtime monitoring for prompt attack patterns contributes post-market monitoring evidence.

Security Command Center detects AI-specific threats across the AI stack.

AuditaAI assessment: continuous AI-threat detection contributes post-market monitoring evidence for deployed systems.

Lasso AI detection and response workflows identify and triage suspicious AI behavior using contextual attack telemetry.

AuditaAI assessment: suspicious-behavior detection and triage workflows contribute post-market monitoring evidence for AI operations.

AI Runtime Threat Detection and Response monitors production AI execution to detect suspicious outputs and attack behavior.

AuditaAI assessment: production attack-behavior monitoring contributes post-market monitoring evidence for deployed AI systems.

Microsoft Agent 365 provides continuous agent threat detection and configurable real-time protection policies that block unsafe behaviors and malicious activity.

AuditaAI assessment: continuous threat detection and policy blocking contribute post-market monitoring evidence for deployed AI behavior.

Data Detection and Response performs real-time scanning with automated quarantine, notification, and remediation workflows for risky content flows.

AuditaAI assessment: real-time detection and remediation of risky content flows contributes post-market monitoring evidence.

Zenity behavioral runtime telemetry supports detection and investigation of unsafe or manipulative AI outcomes.

AuditaAI assessment: runtime detection and investigation workflows contribute post-market monitoring evidence.