Key Takeaways
- AI governance now has to cover actions, not just outputs. Agents retrieve data, call tools, and trigger workflows, so governance must reach access and data movement too.
- Adoption is outpacing control. Most organizations run AI assistants beyond pilot, but confidence in their controls lags, and one-time approvals leave agents unmonitored.
- Permitted is not the same as intended. Access control confirms an agent may act, not that the action fits the task. That gap is where semantic privilege escalation can occur.
- Agent integrity closes the gap frameworks leave. NIST, ISO/IEC 42001, and the EU AI Act set goals and duties but leave technical detail to each organization. Intent-based access control (IBAC) checks each action against the user’s request before it runs.
- Controls must work at runtime and mature in stages. Blocking and approval must happen as agent actions do. A maturity model sequences the work from discovery to enforcement.
An AI governance framework is a set of policies, roles, processes, and controls that guide how an organization builds, uses, and secures AI responsibly. For years, it centered on models: accuracy, bias, privacy, and explainability. Agentic AI governance extends it to AI agents that retrieve data, use tools, and take action, shifting the question from whether a model produces the right output to whether an agent takes the right action.
What is AI governance?
AI governance is the practice of overseeing how an organization develops, deploys, and uses AI so that it stays secure, compliant, and aligned with business goals. It sets accountability, acceptable use, and risk controls. An AI governance framework makes that practice repeatable: the policies, roles, processes, and technical controls teams follow.
Policies, approval workflows, and model documentation still matter, but they cannot show what an AI agent does with its access. As organizations adopt agentic AI, governance has to extend from how models are built to how agents behave, which makes security a core partner alongside legal, compliance, data, and engineering teams.
AI governance is distinct from responsible AI and AI ethics, which set principles such as fairness, transparency, privacy, safety, and accountability, and from AI security, which protects AI systems, users, and data from misuse and compromise. Governance turns principles into enforceable policies, roles, and evidence, and security enforces them.
Why is AI governance important now?
AI governance matters now because AI has moved from pilot to production, and from answering questions to taking actions. Adoption is moving faster than confidence in the controls around it, which turns unmanaged AI into a security, data, and compliance risk.
- 87% of organizations have AI assistants deployed beyond pilot
- 76% are piloting or rolling out autonomous agents
- 52% are not fully confident their controls would detect a compromised AI
Agents raise the stakes because they combine broad access, autonomy, and speed. Many organizations review an agent once, grant access, and stop watching. A single compromised or misconfigured agent can then reach many systems, and every step will look authorized.
To keep up, governance has to become ongoing, visible, and enforceable. Gartner predicts that by 2027, organizations that establish strong foundational controls and deploy continuous, AI-based assurance for agents will see at least 40% fewer operational and compliance incidents than those relying on traditional governance and human oversight.
What is agentic AI governance?
Agentic AI governance, also called AI agent governance, is the practice of controlling how autonomous AI agents access data, make decisions, use tools, and act across enterprise systems. It extends traditional AI governance from model oversight to behavior and execution, treating an agent as an actor, not only a system that produces outputs.
Type
Traditional AI governance
Agentic AI governance
Focus
Models: outputs, bias, accuracy, transparency, responsible use
Agents: behavior, execution, tool use, data access, autonomous actions
Core question
Is the model producing appropriate outputs?
Is the agent taking appropriate actions?
Evidence
Model documentation and test results
Transaction records from request to outcome
Type
Focus
Traditional AI governance
Models: outputs, bias, accuracy, transparency, responsible use
Agentic AI governance
Agents: behavior, execution, tool use, data access, autonomous actions
Type
Core question
Traditional AI governance
Is the model producing appropriate outputs?
Agentic AI governance
Is the agent taking appropriate actions?
Type
Evidence
Traditional AI governance
Model documentation and test results
Agentic AI governance
Transaction records from request to outcome
A generative AI assistant might draft an email or summarize a document. An AI agent can go further: search mailboxes, retrieve SharePoint files, query a CRM, update records, open tickets, or call other applications. Each step uses real access to real systems, and the agent chooses the steps based on how it interprets the user’s request.
Agentic AI governance therefore has to answer six questions:
- Identity: Which person, agent, service account, or token started the action?
- Permissions: What data, tools, systems, and workflows can the agent access?
- Actions: What can the agent send, modify, delete, approve, or trigger?
- Interactions: How does the agent engage with employees, partners, customers, and other agents?
- Tool use: Which APIs, SaaS connectors, Model Context Protocol (MCP) servers, and workflows can it use?
- Data access: What sensitive or regulated data can it retrieve, change, summarize, or share?
That means governing the agent as a privileged non-human identity that acts on behalf of a person, so every action can be traced to both.
What are the key principles of AI governance?
Key principles of AI governance include transparency and explainability, accountability and human oversight, security by design, fairness and bias mitigation, data quality and integrity, compliance and auditability, lifecycle management, and monitoring with incident response. Together they turn responsible AI values into controls an organization can test, monitor, and audit.
Each principle still applies to AI agents, but agents change what it requires in practice.
Principle
What it means
What changes for AI agents
Transparency and explainability
Document how models are trained and reach decisions so stakeholders can review and audit them
Record why an agent took each action, not only what it produced
Accountability and human oversight
Assign owners for AI outcomes, keep people in high-stakes decisions, and define escalation paths
Require human approval for high-risk actions such as external sharing or irreversible changes
Security by design
Build controls in from the start: access control for training data, secure model storage, encrypted APIs, and defenses against attacks such as prompt injection
Scope each agent’s tools, data, and credentials to its purpose
Fairness and bias mitigation
Test for and reduce biased outcomes that could lead to discrimination or regulatory violations
Test agent decisions that affect people, such as hiring or lending workflows
Data quality and integrity
Keep an inventory of datasets and know their sources, contents, access, and use
Govern the data agents retrieve and change at runtime, not only training data
Compliance and auditability
Log model decisions and data use so regulators and internal teams can investigate
Trace each outcome from the user’s request through every tool call
Lifecycle management
Govern each phase of a model’s life: development, versioning, testing, deployment, and retirement
Manage agent versions and configurations the same way
Monitoring and incident response
Watch for drift, harmful or inaccurate outputs, and compromise, with response plans for failures
Monitor actions and tool use in real time, and be able to disable an agent and revoke its access
Principle
Transparency and explainability
What it means
Document how models are trained and reach decisions so stakeholders can review and audit them
What changes for AI agents
Record why an agent took each action, not only what it produced
Principle
Accountability and human oversight
What it means
Assign owners for AI outcomes, keep people in high-stakes decisions, and define escalation paths
What changes for AI agents
Require human approval for high-risk actions such as external sharing or irreversible changes
Principle
Security by design
What it means
Build controls in from the start: access control for training data, secure model storage, encrypted APIs, and defenses against attacks such as prompt injection
What changes for AI agents
Scope each agent’s tools, data, and credentials to its purpose
Principle
Fairness and bias mitigation
What it means
Test for and reduce biased outcomes that could lead to discrimination or regulatory violations
What changes for AI agents
Test agent decisions that affect people, such as hiring or lending workflows
Principle
Data quality and integrity
What it means
Keep an inventory of datasets and know their sources, contents, access, and use
What changes for AI agents
Govern the data agents retrieve and change at runtime, not only training data
Principle
Compliance and auditability
What it means
Log model decisions and data use so regulators and internal teams can investigate
What changes for AI agents
Trace each outcome from the user’s request through every tool call
Principle
Lifecycle management
What it means
Govern each phase of a model’s life: development, versioning, testing, deployment, and retirement
What changes for AI agents
Manage agent versions and configurations the same way
Principle
Monitoring and incident response
What it means
Watch for drift, harmful or inaccurate outputs, and compromise, with response plans for failures
What changes for AI agents
Monitor actions and tool use in real time, and be able to disable an agent and revoke its access
What risks does AI governance address?
AI governance addresses two kinds of risk. Model and data risks, such as data leakage, inaccurate outputs, and bias, apply to any AI system. Agentic AI risks appear when AI can act on its own, and they are the gaps that traditional governance programs were not designed to catch.
Model and data risks
Governance programs have long focused on these risks:
- Data leakage and exposure: Employees paste sensitive data into AI tools, and shadow AI bypasses IT oversight.
- Hallucinations and misleading outputs: Fabricated but convincing content creates liability when it reaches customers or informs decisions.
- Bias and discrimination: Models trained on historical data can repeat bias in hiring, lending, or customer service.
- Intellectual property exposure: Models trained on copyrighted material, and the content they generate, raise ownership and infringement questions.
- Unauthorized access to models and APIs: Attackers who steal model weights or tamper with training data can extract data or steer outputs.
- AI-enabled attacks: Attackers use AI for convincing phishing, deepfakes, and automated reconnaissance.
- Regulatory non-compliance: Regulations increasingly require transparency, risk assessments, and audit evidence, and gaps can bring fines or legal action.
Agentic AI risks: where governance fails
Agents add four recurring failures. Each pairs a gap with the governance response it calls for.
- Over-permissioned agents: Broad access makes agents easier to build, so teams grant full mailbox, SharePoint, or API access, and an agent on a shared service credential can give every user who invokes it the same elevated access. Start with least privilege and delegated, on-behalf-of tokens.
- Hidden instructions treated as trusted: Emails, documents, web pages, tickets, and chat messages can carry malicious instructions that an agent cannot always tell from legitimate ones. Treat outside content as untrusted, and check what the agent does.
- Unmonitored, untraceable actions: Agents can send messages, change records, and trigger workflows, often through other agents, without a clear trail, so teams may not know whether a person or an agent acted. Monitor continuously and keep records that follow the request through every handoff.
- Data leakage through AI workflows: Sensitive data can leave through prompts, retrieved context, summaries, generated files, and downstream sharing, and each connected system checks only its own permissions. Apply data loss prevention and classification across the whole workflow.
Established frameworks and regulations give organizations a place to start on these.
Which frameworks and regulations inform AI governance?
Many frameworks, standards, and laws inform AI governance worldwide. AI governance frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 provide structure, the EU AI Act sets binding rules, and privacy laws such as GDPR and HIPAA apply when AI handles personal or health data.
They work in layers rather than as alternatives: NIST structures risk management, ISO/IEC 42001 provides a management system for accountability and continual improvement, and the EU AI Act sets legal duties where it applies. Agent-specific guidance is also appearing, including the Singapore Model AI Governance Framework for Agentic AI and a NIST initiative on AI agent security and identity. For detail, see our guides to AI risk management frameworks and AI compliance.
These frameworks set goals and duties, leaving the technical detail to each organization. Security teams still need answers as an agent works: Did the action match the user’s intent? Can we reconstruct the full workflow? Can we stop a risky action before it completes? Answering those questions takes another layer: agent integrity.
What is agent integrity in AI governance?
Agent integrity is the assurance that an AI agent operates within the boundaries of its intended purpose, authorized permissions, and expected behavior across every interaction, tool call, and data access. It checks whether what an agent is allowed to do, what it should do, and what it actually does line up. Proofpoint developed the concept in its Agent Integrity Framework white paper.
Access control asks whether an identity has permission to act. Agent integrity asks whether this agent should take this action for this task. Security stops unauthorized access and malicious behavior. Integrity also covers agents that are authorized and not malicious but still stray from the task.
Proofpoint uses two terms for how this goes wrong. Semantic privilege escalation is when an agent uses valid permissions in ways that do not fit its task. Unlike conventional privilege escalation, it gains no new access. Malcontent is malicious instructions hidden in content an agent processes, such as a document, email, or web page. When an agent acts on them without anyone clicking anything, the result is a zero-click attack.
For example, a user asks an agent to summarize an email. The agent instead scans connected storage for API keys and emails them out. Each action passes a permission check, but together they have nothing to do with the task.
Agent integrity can be measured across five pillars:
- Intent alignment: Do the agent’s actions match the user’s request and the agent’s purpose?
- Identity and attribution: Can each action be traced to the person, the agent as a non-human identity, the authority behind it, and the business purpose?
- Behavioral consistency: Is the agent acting within expected patterns?
- Full agent audit trails: Can security teams reconstruct prompts, tool calls, data access, decisions, approvals, and outputs?
- Operational transparency: Can the organization explain and prove agent oversight to security, compliance, legal, and business teams?
The pillars work as a set: a gap in one weakens the rest.
What are the core components of an AI governance framework for agents?
An enterprise AI governance framework for agents needs eight components: AI inventory, identity and access control, data governance, MCP and tool governance, policy-as-code, behavioral monitoring, intent-based runtime enforcement, and transaction forensics with incident response. Together, they let teams see agents, define acceptable behavior, enforce it as agents act, and prove what happened.
AI inventory and discovery
Find every AI tool, agent, MCP server, and connected system in use, including embedded features and shadow AI. For each, record the owner, purpose, users, model, data sources, permissions, and risk level.
Identity and access control for agents
Govern agents as identities, not only applications. Separate human and agent identities, use delegated tokens, review service accounts, and base access on the task, data sensitivity, and workflow context, not role alone.
AI data governance and DLP
Know what sensitive data exists, where it lives, and who or what can access it. Combine classification, data security posture management (DSPM), and data loss prevention across prompts, outputs, sharing, and training or retrieval workflows.
MCP and tool governance
Keep a registry of approved MCP servers and tools, require authentication, inspect content, vet server sources and supply chains, and log every connection so agents cannot reach systems through unreviewed connectors.
Policy-as-code and agent manifests
A manifest is a machine-readable statement of the tools, data sources, and models an agent may use and the actions allowed or blocked. Policy-as-code applies those rules consistently across frameworks, clouds, and deployments
Behavioral monitoring
Baseline each agent’s normal tools, data sources, and timing, then flag deviations, such as a financial analysis agent suddenly reaching HR systems. Monitoring can catch compromise, misconfiguration, and misuse even when access is permitted.
Intent-based access control and runtime enforcement
Capture the user’s intent, monitor each action, check that it fits the request, and enforce policy before it runs: allow, block, log, or escalate. High-risk actions, such as external sharing or irreversible changes, need human approval.
Transaction forensics and incident response
Trace every outcome from the original request through each agent action, tool call, and approval. Pair that record with a plan to disable an agent, revoke tokens, isolate workflows, and determine what data was exposed.
AI inventory and discovery
Find every AI tool, agent, MCP server, and connected system in use, including embedded features and shadow AI. For each, record the owner, purpose, users, model, data sources, permissions, and risk level.
Identity and access control for agents
Govern agents as identities, not only applications. Separate human and agent identities, use delegated tokens, review service accounts, and base access on the task, data sensitivity, and workflow context, not role alone.
AI data governance and DLP
Know what sensitive data exists, where it lives, and who or what can access it. Combine classification, data security posture management (DSPM), and data loss prevention across prompts, outputs, sharing, and training or retrieval workflows.
MCP and tool governance
Keep a registry of approved MCP servers and tools, require authentication, inspect content, vet server sources and supply chains, and log every connection so agents cannot reach systems through unreviewed connectors.
Policy-as-code and agent manifests
A manifest is a machine-readable statement of the tools, data sources, and models an agent may use and the actions allowed or blocked. Policy-as-code applies those rules consistently across frameworks, clouds, and deployments
Behavioral monitoring
Baseline each agent’s normal tools, data sources, and timing, then flag deviations, such as a financial analysis agent suddenly reaching HR systems. Monitoring can catch compromise, misconfiguration, and misuse even when access is permitted.
Intent-based access control and runtime enforcement
Capture the user’s intent, monitor each action, check that it fits the request, and enforce policy before it runs: allow, block, log, or escalate. High-risk actions, such as external sharing or irreversible changes, need human approval.
Transaction forensics and incident response
Trace every outcome from the original request through each agent action, tool call, and approval. Pair that record with a plan to disable an agent, revoke tokens, isolate workflows, and determine what data was exposed.
What does agentic AI governance look like in practice?
Take an AI assistant in Microsoft 365 that can reach email, Teams, SharePoint, and calendars. Governance follows it from approval to audit, defining what it should do, monitoring what it actually does, and enforcing controls when intent and action diverge.
- At approval: The team defines its purpose: summarizing meeting threads and reading specific SharePoint folders. It cannot send external email or share files without approval.
- At setup: It runs on delegated user permissions, not a broad service account, and its allowed tools and data sources are recorded in a manifest.
- When a request arrives: A user asks for a summary of a meeting thread, and the system records that intent.
- As the agent works: A shared document contains hidden text telling it to email the summary to an outside address. The action does not match the request, so it is stopped and flagged for human review.
- Afterward: The full trace, from request to stopped action, is logged for the security team.
What is an AI governance maturity model for agents?
An AI governance maturity model shows how far an organization’s control over AI has progressed and what to build next. For agents, the Agent Integrity Maturity Model from Proofpoint describes five levels: legacy and ad hoc, discovery, governance, detection, and runtime enforcement.
At each level it tracks six areas: inventory, identity and access, policy, intent, forensics and audit, and MCP security. Download The Agent Integrity Framework for the full level-by-area table.
Level 1: Legacy and ad hoc
Agents appear without review. Controls rely on permission checks and generic data security, service accounts are used broadly, and no one has a reliable inventory of agents or MCP servers.
Level 2: Discovery
Teams know which agents, models, and MCP servers exist, can tell human actions from agent actions, and log agent transactions centrally to establish a baseline of normal behavior.
Level 3: Governance
Agents are classified by risk. Manifests define allowed tools and data, a delegated-token strategy is set, approved MCP servers are registered, and logs carry security context.
Level 4: Detection
Behavioral anomaly detection, credential monitoring, and full transaction tracing run in visibility mode (alerting without blocking), with continuous monitoring for rogue agents and supply chain vetting for MCP servers.
Level 5: Runtime enforcement
Intent-based access control runs inline and policies block violations in real time. Unapproved agents and servers are blocked, delegated tokens are enforced, and multi-agent tracing supports automated regulatory reporting.
Level 1: Legacy and ad hoc
Agents appear without review. Controls rely on permission checks and generic data security, service accounts are used broadly, and no one has a reliable inventory of agents or MCP servers.
Level 2: Discovery
Teams know which agents, models, and MCP servers exist, can tell human actions from agent actions, and log agent transactions centrally to establish a baseline of normal behavior.
Level 3: Governance
Agents are classified by risk. Manifests define allowed tools and data, a delegated-token strategy is set, approved MCP servers are registered, and logs carry security context.
Level 4: Detection
Behavioral anomaly detection, credential monitoring, and full transaction tracing run in visibility mode (alerting without blocking), with continuous monitoring for rogue agents and supply chain vetting for MCP servers.
Level 5: Runtime enforcement
Intent-based access control runs inline and policies block violations in real time. Unapproved agents and servers are blocked, delegated tokens are enforced, and multi-agent tracing supports automated regulatory reporting.
Each step has a starting point: inventory and map agents (Level 2), classify risk and define policies and manifests (Level 3), add security-annotated logging and behavioral detection (Level 4), and enforce inline for the highest-risk workflows (Level 5).
The six areas should mature together. An organization with strong MCP security but no inventory or identity attribution has a false sense of security. The goal is not Level 5 everywhere at once but to know where you stand, close the biggest gaps first, and build according to risk and regulatory requirements. Track progress with measures of coverage, control, and evidence: the share of agents inventoried and risk-classified, the share with defined policies or manifests, blocked or escalated actions, time to detect and respond to violations, and audit trail completeness.
Teams commonly stall on three things: too few people who understand both AI and governance, pressure not to slow delivery, and manual processes that do not scale.
Who is responsible for AI governance?
AI governance needs cross-functional ownership with a named owner for every decision. Security, engineering, legal, compliance, HR, and employees each hold part of the work, and every AI system or agent needs a named owner accountable for what it does.
- CISO and security teams own the security posture of AI systems: data protection safeguards, incident response for AI-related breaches, and threat modeling for attacks such as data poisoning.
- CTO and engineering leaders own development standards, model testing and validation, and registries of the AI systems in use.
- Compliance and legal translate regulations into policy and maintain the audit trails regulators expect.
- HR and internal communications own acceptable use policies, train employees on sanctioned AI tools, and communicate policy changes.
- Employees use only sanctioned tools, keep sensitive data out of public AI, and report violations.
What should you look for in AI governance tools?
Look for AI governance tools that discover the agents and AI tools in use, understand what each one is trying to do, and stop risky actions before they complete. Tools in this space include model monitoring, policy management, data loss prevention, identity and access management, API and MCP gateways, logging and forensics, and data security posture management.
Ask these questions when comparing AI governance platforms and solutions:
- Coverage. Does it cover employee AI use, agents, and MCP connections across email, SaaS, cloud, collaboration tools, and endpoints, or only one channel?
- Independence from how agents are built. Does it work across frameworks, models, clouds, and existing gateways without developers instrumenting their code?
- Intent, not just content or traffic. Can it show what an agent is trying to do and whether the action fits the request?
- Enforcement, not just detection. Can it block, hold for approval, or flag risky actions before they complete?
- Data context. Does it connect to sensitive data classification and data loss prevention across prompts, context, outputs, and sharing?
- Evidence and integration. Does it keep full transaction records and feed SIEM, SOAR, identity, and incident response workflows?
What is the future of AI governance?
The future of AI governance is being shaped by agents that delegate to other agents, connect through shared protocols, and act on physical as well as digital systems. Governance will need to follow actions across those chains and produce evidence of what happened, not just review policies.
Five trends stand out:
- Agents that delegate to agents. In multi-agent workflows, the original user’s identity and intent have to carry through every handoff.
- More protocols, same gaps. MCP is now joined by agent-to-agent protocols such as A2A, and each tends to be designed for capability first, with security left to whoever deploys it. One set of controls across protocols works better than a new gateway for each.
- Identity governance for agents. Agents become part of identity programs as non-human identities, with attribution that separates human actions from agent actions.
- Converging disciplines. Cybersecurity, data governance, and AI governance are increasingly treated as one risk program, because AI security depends on data protection and data governance needs AI-specific controls.
- Physical consequences. As AI reaches operational technology in manufacturing, energy, and transportation, governance failures can cause safety incidents, not only data breaches.
The organizations that succeed with agents will not be those that deploy the most agents the fastest. They will be the ones that can prove their agents act securely, appropriately, and in line with business intent.
How Proofpoint helps you enforce AI governance
Governance defines what should happen, and security enforces it where work happens. Proofpoint AI Security applies the agent integrity approach described above across employee AI use, AI agents, and MCP connections, and Proofpoint data security protects the data AI touches.
- AI Access Security discovers AI tools in use, including shadow AI, inspects employee interactions at runtime, enforces context-aware policies, and produces audit-ready evidence.
- Agentic AI Security governs agent behavior with intent-based detection, runtime visibility across multi-step workflows, and behavioral anomaly detection, and reconstructs agent transactions from request to outcome.
- AI MCP Security enforces authentication and content inspection at the MCP boundary, keeps a registry of approved servers, and checks the security posture of services in the AI supply chain.
- Data Security for AI discovers sanctioned and unsanctioned AI use, monitors prompts, uploads, and responses for sensitive data, and can block, warn, redact, or restrict activity based on policy.
FAQ
AI governance matters because AI has moved from pilot to production, and agents now act on data and systems instead of only answering questions. The 2026 AI and Human Risk Landscape report from Proofpoint found that 87% of organizations run AI assistants beyond pilot and 52% are not fully confident their AI security controls would detect a compromised AI.
AI governance sets the rules for AI use: accountability, acceptable use, risk controls, and compliance. AI security enforces those rules by protecting AI systems, users, and data from misuse and compromise. Each depends on the other. Rules without enforcement stay on paper, and enforcement without rules has nothing to apply.
Agentic AI governance, also called AI agent governance, is the practice of controlling how autonomous AI agents access data, make decisions, use tools, and act across enterprise systems. It extends traditional AI governance from reviewing models and outputs to overseeing agent behavior, so teams can confirm an agent takes the right actions, not only gives the right answers.
An AI governance framework should include defined roles and policies plus controls for AI inventory, risk classification, data governance, access, monitoring, audit trails, and incident response. For AI agents, add identity and tool governance, including MCP, policy as code, intent-based runtime enforcement, behavioral monitoring, and transaction forensics.
Agent integrity is the assurance that an AI agent operates within the boundaries of its intended purpose, authorized permissions, and expected behavior across every interaction, tool call, and data access. Proofpoint developed the concept in its Agent Integrity Framework white paper to help teams verify that an agent is not only allowed to act, but acting appropriately for its task.
Govern privileged access for AI agents as you would any privileged non-human identity, with added checks on intent. Grant least privilege, use delegated on-behalf-of tokens instead of shared service credentials, keep human and agent identities separate, review access regularly, and verify that each action fits the user’s request before it runs.
Useful AI governance metrics show coverage, control, and evidence: the share of agents inventoried and risk-classified, the share with defined policies or manifests, blocked or escalated actions, time to detect and respond to violations, and audit trail completeness. Tracking progress against a maturity model shows where governance is still weak.
Look for a platform that discovers the AI tools and agents in use, understands what each is trying to do, and can stop risky actions before they complete. Check its coverage across employees, agents, and MCP connections, whether it works regardless of how agents are built, and whether it feeds SIEM, SOAR, and incident response workflows.