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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →To audit an AI agent, identify who owns it, trace its identity and permissions through every tool and connected service, verify authorization at the moment each action runs, and reconstruct its activity from correlated logs. Then test whether you can promptly revoke access all the way downstream. A role name or list of enabled tools alone cannot tell you what the agent can actually do.
How do I audit AI agent permissions and activity?
Use a repeatable review that follows the agent from its owner and identity to each tool call and the service that ultimately accepts or rejects it. This is security-control guidance, not a certification standard or legal advice; tailor the audit fields, approval thresholds, log retention, and review frequency to your architecture and applicable policies.
- Inventory the agent. Record its stable identifier, accountable owner and approver, purpose, environment, platform, data handled, tools and connectors, downstream services, and whether it acts independently or for a person. Include planned agents and cross-tenant or guest integrations. Microsoft recommends a centralized registry with explicit ownership; AWS recommends dedicated, consistently tagged agent roles. Microsoft Learn; AWS Prescriptive Guidance
- Trace identity and access end to end. Record the agent principal, authentication method, credential owner, token lifetime, delegated-user context, role assignments, resource scope, and trust relationships. Follow each tool call to the downstream service. Keep the agent distinguishable from the human requester; when it acts for a user, securely propagate user context rather than giving it that person’s credentials. Calculate effective access across the full chain, not role by role. AWS Prescriptive Guidance; Microsoft Learn
- Test the action boundaries. For each tool, document permitted operations, resources, parameters, and data scopes. Deny unreviewed tools by default, separate read from write where practical, and confirm each action is checked at execution time and by the downstream service—not only when a session starts. Use approval or time-limited elevation for irreversible, financial, administrative, externally visible, or production-changing actions. Bind each approval to the actor, tool, target, parameters, and expiry, and independently validate it before execution. Fail closed if policy, approval, risk classification, or audit logging cannot be checked. OWASP AI Agent Security Cheat Sheet
- Reconstruct real activity. Sample routine and sensitive executions. Follow the initiating identity through the orchestrator and tool to the downstream service. Confirm records show the agent and owner, user context if applicable, effective scope, tool and action, target, timestamp, authorization and approval outcome, and a correlation identifier. Check for failed actions and permission changes as well as successful calls, and make sure agent activity is distinguishable from human activity. AWS Prescriptive Guidance; Microsoft Learn
- Monitor and test containment. Watch for unexpected resource access, newly enabled tools or permissions, unusual action patterns, repeated denials or bypass attempts, and scope expansion. Reassess after material changes to the workflow, tools, data, or deployment. Test disabling the identity, rotating or invalidating credentials, removing stale assignments, and confirming that downstream services reject subsequent requests. Protect logs and retain only what is needed under applicable organizational and legal requirements; the sources do not establish a universal retention period. AWS Prescriptive Guidance; Microsoft Learn
What permissions should an AI agent have?
Give an agent a distinct, accountable identity and only the access its approved task requires. Avoid shared human credentials: they make it harder to establish who acted and to contain access. Least privilege must be evaluated across the full chain, because individually narrow roles, tools, and connectors can combine into excessive end-to-end capability.
- Check for broad standing identities, shared accounts, role chaining into human roles, stale assignments, cross-tenant access, and tools enabled without an approved purpose.
- Allow only documented operations, resources, parameters, and data scopes; separate read and write access where feasible.
- Validate the actor, action, and target for every execution. Tool availability is not permission to use a tool.
- Use independent authorization checks and appropriate human approval for high-impact actions; make approval specific to the requested action and time-limited.
AWS and Microsoft both recommend distinct agent identities and least privilege. Microsoft’s guidance states, “Regardless of deployment model, you’re always accountable for:” and then identifies responsibilities including data, identity and least privilege, action authorization, human oversight, and governance. This is vendor guidance, not a legal conclusion. AWS Prescriptive Guidance; Microsoft Learn
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How can I see what an AI agent did?
Start with the agent’s identity and a time window, then use correlation identifiers to connect its orchestrator record to tool and downstream-service events. A useful trail lets an auditor answer who initiated the work, which agent acted, under what effective scope, what action targeted which resource, whether authorization and approval succeeded, and what the connected service recorded.
The implementation depends on the environment. AWS guidance describes CloudTrail attribution and Athena analysis for AWS deployments. Microsoft guidance points to Entra audit logs and application permission activity logs within its ecosystem. These are platform-specific examples; they are not interchangeable products or universal requirements. Verify actual logging and attribution in the service you use. AWS Prescriptive Guidance; Microsoft Learn
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How does the audit change across deployment models?
Do not assume a cloud provider or SaaS vendor supplies every control simply because it operates part of the agent stack. Microsoft’s shared-responsibility guidance distinguishes IaaS, PaaS, and SaaS: customers remain accountable for data, identity, authorization, human oversight, and acceptable use, while responsibility for specific tool permissions, delegated tokens, action checks, and action logging varies by model. Confirm the control in the actual service and identify who operates it.
- Identity and permissions: Determine who creates the agent identity, grants tool and data access, and reviews changes.
- Action authorization: Verify where each action is checked and whether downstream services independently enforce access.
- Traceability: Confirm which component records the action and whether its logs can be correlated across the chain.
- Revocation: Establish who can disable the agent and invalidate credentials, then test that downstream access ends.
- Data and logs: Identify who governs the data handled and the access, protection, and retention of activity records.
Microsoft Learn: AI agent shared responsibility model
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