The Tool Desk
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How do I audit AI agent permissions and activity?
Use a repeatable review that follows the agent from its owner and identity through its tools to the systems those tools can affect. A role name alone does not show the agent’s full capability: individually narrow permissions can combine into broad end-to-end access.
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- 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 guest and cross-tenant integrations.
- Trace its identity and permission chain. Document the principal, authentication method, credential owner, token lifetime, delegated-user context, role assignments, resource scope, and trust relationships. Follow each tool call into the service it reaches, then calculate the agent’s aggregate effective access.
- Test action authorization. For each tool, specify permitted operations, resources, parameters, and data scopes. Check that every action is authorized at execution time and again by the downstream service where applicable; do not treat a tool being available or a session being authenticated as permission for every action.
- Reconstruct activity. Sample routine and sensitive executions. Trace the initiating identity through the orchestrator, tool, and downstream service, checking successful and failed actions, approvals, and permission changes.
- Monitor and contain. Review for unexpected access, new tools or grants, unusual action patterns, repeated denials or bypass attempts, and scope expansion. Reassess after material changes and test disabling the identity, invalidating credentials, removing stale access, and confirming that downstream services reject subsequent requests.
What permissions should an AI agent have?
Give an agent a distinct, accountable identity and only the access required for its defined task. Avoid shared human credentials: they blur attribution and make containment harder. AWS and Microsoft both recommend distinct agent identities and least privilege. Microsoft also advises organizations to maintain a centralized registry and explicit ownership; AWS describes dedicated, consistently tagged agent roles.
Review effective access, not just individual roles
Map permissions across the agent’s identity, roles, tools, connectors, and downstream systems. Look for shared or broad standing identities, stale assignments, cross-tenant access, role chains into human roles, and tools enabled without an approved purpose. The key question is what the full chain lets the agent do—not whether each assignment looks narrow in isolation.
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Set and enforce action boundaries
- Deny unreviewed tools by default, and separate read from write access where practical.
- Define allowed operations, targets, parameters, and data scopes for each tool.
- Require approval or time-limited elevation for irreversible, financial, administrative, externally visible, or production-changing actions.
- Bind an approval to the exact actor, tool, target, parameters, and expiry. The component executing the action should independently validate that approval and the authorization.
- Fail closed if policy lookup, approval validation, risk classification, or audit logging fails.
When an agent acts on a person’s behalf, preserve that user context securely rather than giving the agent the person’s credential. Keep the agent identity distinguishable from the human requester.
How can I see what an AI agent did?
Start with the agent identity and follow a single execution across the orchestrator, tool, and downstream service. A useful audit trail lets a reviewer connect who or what initiated an action, the authority used, what was attempted, which resource was targeted, whether approval was granted, and what happened downstream.
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Check that records contain the context to reconstruct an action
- Agent identifier and accountable owner
- Acting user context, when the agent acts for a person
- Role or effective scope used for the operation
- Tool, action, target resource, and timestamp
- Authorization and approval outcomes
- A correlation identifier propagated across the agent, tool, and downstream events
- Failed actions and permission changes, as well as successful calls
Verify that agent activity can be distinguished from human activity. A log of tool calls without identity, target, authorization outcome, or correlation context may show that something happened without establishing who authorized it or what system was affected.
Use platform-specific logs without assuming they are interchangeable
For AWS implementations, AWS guidance describes CloudTrail for attribution and Athena for analysis. In Microsoft environments, Microsoft points to Entra audit logs and application permission activity logs. These are examples for their respective ecosystems, not universal requirements; confirm which events the actual services record and whether they can be correlated across the deployment.
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How should the audit differ by deployment model?
Cloud responsibility changes by model, but it does not remove the need to verify controls in the deployed service. Microsoft Learn’s “AI agent shared responsibility model” states, “Regardless of deployment model, you’re always accountable for:” its guidance lists data, identity and least privilege, action authorization, human oversight, and governance. This is vendor guidance, not a legal conclusion.
| Deployment model | What to establish in the audit |
|---|---|
| IaaS | Verify how the customer configures and enforces identity, tool permissions, delegated tokens, action checks, and action logging in the deployed stack. Microsoft’s model distinguishes responsibility by deployment type; confirm the specific controls rather than assuming the provider supplies them. |
| PaaS | Check which identity, permission, action-check, and logging controls are provided by the platform and which remain the customer’s responsibility. Test the actual configuration and downstream enforcement. |
| SaaS | Confirm what activity and permission events the service exposes, what the customer can configure, and how agent actions are attributed and revoked in connected systems. Do not infer that provider-managed service operation covers customer data, identity, authorization, oversight, or governance. |
The precise allocation for tool permissions, delegated tokens, per-action checks, and action logs depends on the service and deployment. Microsoft’s responsibility matrix is a starting point; service-specific controls must be checked in the environment being audited. AWS and Microsoft examples describe their own ecosystems and should not be treated as substitutes for a cross-platform control standard.
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How do I keep the audit useful over time?
Agent configurations change: workflows evolve, tools are added, data sources shift, and integrations expand. Set access reviews according to the pace of change and organizational risk, and trigger reassessment after a material change to workflow, tools, data, or deployment.
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- Test the full containment path: disable the agent, rotate or invalidate credentials, remove stale permissions, and verify downstream services re-authorize requests.
- Protect audit logs and retain only the data needed for security, operational, and governance purposes. Set retention according to applicable organizational and legal requirements; vendor guidance reviewed here does not establish a universal retention period.
This procedure is security-control guidance, not a certification standard or legal advice. Tailor audit fields, approval thresholds, retention, and review timing to the architecture and applicable policy.
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