To audit an AI agent, inventory its identity and owner, trace its effective access through every tool and downstream service, test authorization at the point of action, and reconstruct what it did from correlated logs. Then check that monitoring, access reviews, and revocation work in practice. Review permissions as a complete chain—not as isolated role names.
How do I audit AI agent permissions and activity?
Use a repeatable review that connects each agent to an accountable owner, a defined purpose, the identity it uses, the data and tools it can reach, the actions it takes, and the controls that limit those actions. Apply it to production agents and planned deployments, and repeat it when the agent or its environment changes.
- Inventory agents and ownership. Record a stable agent name or identifier, accountable owner and approver, business purpose, deployment environment, platform, data handled, tools and connectors, downstream services, and whether the agent acts independently or for a person. Include guest and cross-tenant integrations. Microsoft recommends a centralized agent registry and explicit ownership; AWS recommends dedicated, consistently tagged agent roles. See Microsoft’s agent identity guidance and AWS agent permissions guidance.
- Map identity and the full permission chain. For each agent, document its principal, authentication method, credential owner, token lifetime, delegated-user context, role assignments, resource scope, and trust relationships. Follow every tool call into the service that ultimately performs the operation. Include permissions inherited through connectors, role chaining, and downstream integrations.
- Calculate effective access. Assess what the agent can do when all its roles, tools, connectors, and downstream permissions are considered together. Look for broad standing identities, shared accounts, stale assignments, cross-tenant access, tools without an approved purpose, and paths into human roles. Several narrow grants can combine into excessive end-to-end capability.
- Test authorization and action boundaries. For every tool, specify allowed operations, resources, parameters, and data scopes. Deny unreviewed tools by default, separate read from write access where practical, and confirm that authorization is checked on each action and by the downstream service—not only when a session begins. Use allowlists and independent checks for high-impact actions.
- Reconstruct activity from records. Sample ordinary and sensitive executions. Trace each from the initiating identity through the orchestrator and tool to the downstream service. Confirm that successful and failed actions, approvals, and permission changes are visible and attributable.
- Monitor, review, and test containment. Watch for unexpected resource access, new tools or grants, unusual action patterns, repeated denials or bypass attempts, and expanding scope. Set access reviews according to the pace of change and organizational risk; reassess after a material change in workflow, tools, data, or deployment. Test disabling the agent, invalidating credentials, removing stale permissions, and confirming that downstream services reject requests.
What permissions should an AI agent have?
Give an agent a distinct, accountable identity and only the permissions its approved task requires. Avoid shared human credentials: they make it harder to attribute actions to an agent and to contain its access. AWS and Microsoft both recommend distinct agent identities and least privilege. See AWS guidance and Microsoft guidance.
Separate the agent from the person it serves
Record the agent principal separately from the human requester. When an agent acts on someone’s behalf, propagate the user’s context securely rather than giving the agent that person’s credential. Document whether access is independent or delegated, and ensure the downstream service can identify the relevant actor and context.
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Grant access by task, resource, and operation
Define the allowed operations, resource targets, parameters, and data scopes for each tool. Avoid treating tool availability as permission: an agent’s ability to invoke a tool does not itself authorize a specific action. Validate the actor, requested action, and target when the action is executed, and enforce the decision at the downstream service as well.
For irreversible, financial, administrative, externally visible, or production-changing operations, require an appropriate approval or time-limited elevation. Bind the approval to the exact actor, tool, target, parameters, and expiry; have the execution component independently validate both authorization and approval. Fail closed if policy lookup, approval validation, risk classification, or audit logging fails.
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Review the whole chain, not just each role
Trace how identity, roles, tools, connectors, and downstream services combine. A role that appears narrow in isolation may participate in a chain that grants broader access. Check trust relationships and cross-tenant access as well as direct assignments, and remove stale or unjustified grants.
How can I see what an AI agent did?
Start with records that connect the initiating identity to the final operation. A useful audit trail lets a reviewer determine who or what acted, under what authority, on which target, when, with what approval outcome, and how the event relates to downstream activity.
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Check for attributable, correlated records
For sampled executions, confirm that records expose the agent and owner, acting-user context where applicable, role or effective scope, tool and action, target resource, timestamp, authorization and approval outcome, and a correlation identifier. Propagate correlation identifiers across the orchestrator, tools, and downstream services so a reviewer can connect related events. Make agent actions distinguishable from human actions, and check for both successful and failed operations as well as permission changes.
Use the platform’s audit sources, then verify coverage
For AWS implementations, AWS describes CloudTrail for attribution and Athena for analysis. For Microsoft environments, Microsoft points to Entra audit logs and application permission activity logs. These are platform-specific examples, not interchangeable services or universal requirements. Verify in the actual deployment that the records capture the agent’s complete path, including relevant downstream events. See AWS agent permissions guidance and Microsoft agent identity guidance.
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How do responsibilities differ across deployment models?
Do not assume the hosting provider enforces every control. Microsoft’s “AI agent shared responsibility model” says, “Regardless of deployment model, you’re always accountable for:” its list includes data, identity and least privilege, action authorization, human oversight, and governance. That is vendor guidance, not a legal conclusion. Microsoft distinguishes IaaS, PaaS, and SaaS: customer responsibility remains for core accountability, while responsibility for specific tool permissions, delegated tokens, action checks, and action logging varies by model. Confirm each control in the service and architecture actually deployed. See Microsoft’s shared responsibility guidance.
What should an ongoing audit test?
- Permission drift: Identify new roles, tools, connectors, resource scopes, or trust relationships, and confirm each has an approved purpose.
- Unexpected activity: Investigate unusual resource access, action patterns, repeated denied actions, and attempted policy bypasses.
- Change-triggered reassessment: Revisit access after changes to the agent’s workflow, tools, data, or deployment, not only on a fixed calendar.
- Revocation: Disable the identity, rotate or invalidate credentials, remove stale permissions, and verify downstream services re-authorize requests rather than continuing to accept existing access.
- Log protection and retention: Keep only necessary log data, protect it, and set retention to meet applicable organizational and legal requirements. The reviewed vendor guidance does not establish one universal retention duration.
This is security-control guidance, not a certification standard or legal advice. Tailor audit fields, review timing, approval thresholds, and retention to the architecture and applicable policy; the cited guidance does not establish a single cross-platform log schema or universal review cadence.
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