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Audit an AI agent by tracing its full chain of identities and capabilities—not by inspecting its prompt alone. Identify who owns it, which identity runs it, what credentials and tools it can use, which data and downstream systems those tools reach, and whether each action is authorized for the user or workload that initiated it. Then test the boundaries, trace actions in logs, and prove that access can be revoked.
This guide applies to agents that use tools, retrieve or store information, call APIs, or delegate work to other agents. The exact controls depend on the platform and identity model, but the audit questions are the same: what can the agent actually do, on whose authority, against which resources, and how would you know if that changed?
What does an AI agent permissions audit need to establish?
A permissions audit should establish the agent’s effective access across its entire execution path. A model’s prompt, a tool’s name, or a single role assignment cannot answer that by itself: an agent may use a runtime identity, call several tools, access a retrieval index, and then make a request to a separate API under another identity.
The audit should leave you with evidence for four questions:
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- Authority: Which identity is acting—the signed-in user, a dedicated application or workload identity, or a chain of identities?
- Reach: Which tenants, resources, records, data classes, fields, and operations are available through direct and indirect paths?
- Enforcement: Where is each action authorized, and does the downstream system independently check the identity and requested operation?
- Control: Can you trace an action to its initiating request, require approval for consequential actions, and stop access promptly?
Microsoft Learn recommends a unique, dedicated agent identity with a named owner or sponsor and approver. That is a useful starting point, but a distinct identity is not proof of least privilege: the audit must still determine what the identity and every tool it can invoke are permitted to do.
How should I scope the audit before checking access?
Write down what the agent is approved to do and where it runs before enumerating permissions. Without a defined purpose and boundary, it is difficult to decide whether a permission is necessary or excessive.
Record the owner, purpose, and environment
Document a named owner or sponsor, an approver, the business purpose, approved tasks, operating environment, data classes, and tool dependencies. Give the agent a dedicated identity rather than letting it inherit a developer’s personal credentials. Record the relevant tenant or tenants and whether guest or cross-tenant access is in scope.
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Make the approved task description specific enough to compare with actions. “Help with customer support” is less useful than identifying whether the agent may search approved support material, read a customer’s ticket, draft a response, send it, or change account details. The audit should distinguish permitted assistance from authority to make a final change.
Set the boundary and audit date
State which deployment, version or configuration, workflow, and data stores are being assessed, and when the review took place. Include subordinate agents and any environment that can reach the same tools or credentials. If multiple tenants, environments, or versions differ, assess them separately instead of treating one result as universal.
Keep the scope tied to the actual deployment. A configuration review can identify assigned access, but it cannot by itself establish how a runtime behaves under every request. Pair configuration evidence with tests of representative allowed and denied actions.
How do I inventory everything the agent can reach?
Build an access inventory from the runtime outward, following every identity, credential, tool, data store, and downstream service the agent can reach. Include indirect paths and subordinate agents; an inventory limited to visible tools can miss the capability that actually grants access.
Inventory identities and credentials
- Model and runtime identities, including service principals, managed identities, OAuth clients, and any delegated user context.
- OAuth scopes, IAM or RBAC roles, group memberships, role assignments, and resource policies.
- Secrets, certificates, API keys, tokens, and mechanisms that obtain or renew them.
- Personal or developer credentials, shared accounts, and credentials passed between services.
Inventory tools, data, and network paths
- Plugins, tool definitions, MCP servers, and APIs, including their allowed operations and the identity they use.
- Memory stores, retrieval indexes, files, databases, SaaS applications, and other data sources.
- Network destinations, connectors, proxies, and service-to-service hops that can make a resource reachable.
- Subordinate agents and any cross-tenant or guest path used by the agent or its tools.
For each entry, record its owner, environment, identity, resource boundary, granted operations, data class, tenant, and how access is enforced. Note whether a tool receives the initiating user’s context or uses its own backend credential. This turns a list of components into a map of authority.
How do I calculate the agent’s effective permissions?
Calculate what the agent can accomplish by combining permissions across identities, tools, and downstream systems. A set of individually narrow permissions can still enable a broad outcome when tools can be chained or one tool’s output can be used as another tool’s input.
For each reachable resource, record the boundary at which access is limited: tenant, resource, collection, record, field, operation, or time. Then consider both direct permissions and compositions—for example, whether the agent can search one store, export results through another tool, and send or modify them through a third.
AWS guidance warns that agents may chain tools or combine low-privilege tools into high-privilege outcomes. Therefore, do not judge permissions only by each tool’s role in isolation. Assess the possible sequence of calls, destinations, and resulting changes that the agent can make.
Use an action-and-resource matrix
| Audit dimension | What to record | Question to test |
|---|---|---|
| Identity model | Delegated user, app-only identity, managed identity, or service principal | Whose authority is applied at each hop? |
| Scope granularity | Tenant, resource, collection, record, field, operation, and time boundaries | Can access be narrowed to the exact target and action? |
| Tool controls | Allowlist, argument schema, read/write separation, and policy enforcement | Can an input change the target or operation beyond the approved task? |
| Human control | Approval or step-up requirements for consequential actions | Does approval cover the specific action and target? |
| Observability | Correlation across the request, agent, tool, and downstream logs | Can an operator reconstruct who requested and authorized the action? |
| Containment | Token invalidation, kill switch, rollback, and recovery procedure | Can access be stopped, and is recovery tested? |
| Deployment boundary | Single tenant, multiple tenants, or guest access | Can a request cross a boundary the owner did not intend? |
Mark each tested action as allowed or denied, and retain the identity, target, operation, and evidence for the result. Where permissions depend on a particular user, tenant, or condition, test that condition explicitly instead of generalizing from one successful request.
Is the agent using the user’s permissions or its own?
Check the identity on every tool call and downstream request; do not infer it from the chat interface or the agent’s configuration screen. User-owned data should be accessed in a way that preserves the signed-in user’s authorization and ties the action to that request. An unattended job needs a workload or application identity restricted to the job’s minimum required scope.
For delegated access, verify that a user who is not allowed to access a resource cannot get the agent to retrieve or change it. Confirm that the downstream service sees and authorizes the relevant user context, and that the resulting record is attributable to the initiating request.
For app-only or workload access, identify the identity and its permissions independently of any end user. Check whether the scope is limited to the job’s approved resources and operations. A backend identity must not be used to bypass the user’s permissions: if the user cannot perform an action, the agent should not silently perform it with broader backend authority on that user’s behalf.
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How do I audit tool permissions and inputs?
Allow only the tools and operations needed for the approved task, and enforce the limits at the tool backend or downstream authorization layer. A prompt that tells an agent not to use a tool is not an access-control boundary.
Review tools by operation
- A search or retrieval tool should be read-only and restricted to approved collections.
- A CRM tool should be limited to the allowed customer scope and fields; a need to update one field does not justify broad administrative access.
- A ticket tool may need permission to create or update tickets without permission to delete tickets or administer the system.
Separate read credentials from write credentials where feasible, so a retrieval function cannot use the same authority to change records. Keep the tool allowlist specific to the agent’s task rather than making all available tools callable by default.
Constrain arguments and targets
Use typed input schemas and validate arguments on the backend. Check that the caller cannot supply a different tenant, resource, record, field, operation, or destination than the policy permits. Apply resource constraints and deny by default when the request is malformed, outside scope, or lacks an authorization decision.
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Test inputs that are valid in form but unauthorized in context, such as a permitted operation aimed at a different customer or tenant. The backend should reject them even if the model constructs the call, the user asks for it, or a prompt instruction appears to allow it.
How should I check tokens, roles, and secrets?
Review how credentials are issued, scoped, stored, exposed, and revoked. Look for long-lived or broadly scoped credentials, access inherited from a human account, and any route through which the agent can inspect unrelated secrets or runtime configuration.
- Prefer short-lived or just-in-time tokens when supported, and verify that OAuth scopes match the task.
- Review IAM and RBAC assignments for excess roles, inherited group access, and stale grants.
- Store client secrets in a secure secret manager rather than code or environment variables; AWS recommends Secrets Manager or an equivalent approach.
- Check whether the agent or an exposed tool can read its own environment, configuration, shell history, or unrelated secret stores.
- Record how credentials rotate and what downstream sessions or tokens must be invalidated during revocation.
A credential is not adequately constrained merely because it is stored securely. Storage protects the secret from exposure; narrow scope and limited lifetime reduce the authority available if it is exposed or misused.
How do I test whether downstream systems enforce access?
Verify that each API or data store checks the identity, tenant, audience, resource, and operation for every tool call. An orchestrator’s approval or policy decision is not enough if a downstream service trusts the call without checking what it is allowed to do.
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Also test whether a tool can be called directly or through an alternate route that bypasses the agent’s front-end checks. The audit should establish which layer is authoritative and that it consistently rejects an out-of-scope target or operation.
When should an AI agent require human approval?
Require fresh approval or step-up authentication for high-impact or irreversible actions, including sending, deleting, purchasing, exporting, deploying, or changing permissions. Microsoft Learn recommends binding tool calls to the initiating identity, authorizing the exact action and target, using narrowly scoped tokens, and obtaining fresh human confirmation for such actions.
Approval should be tied to the actor, tool, target, normalized parameters, timestamp, and expiry. Protect it against replay so that a valid approval cannot be reused for a different target, changed parameters, or a later request. If the policy check, approval service, or audit logging is unavailable, fail closed rather than proceeding without the required control.
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What evidence should agent audit logs contain?
Logs should let an authorized reviewer connect the original request to the final outcome across user, agent, tool, and downstream system. The Canadian Centre for Cyber Security and partner agencies recommend continuous monitoring, comprehensive logs, anomaly detection, source checks, human review, and regular security assessments.
For an action, preserve the original request reference, initiating user, agent identity, role and effective scope, policy decision, tool call, target resource, correlation ID, downstream authorization result, and final outcome. Use a correlation identifier that remains traceable across service boundaries; otherwise, separate system logs may not establish that they describe the same action.
Monitor prompts, tool calls, memory interactions, privilege changes, unusual scope expansion, and signs of goal drift. Logs should support detection and investigation without becoming an uncontrolled copy of sensitive data: define who can inspect them and protect them according to the data they contain.
Do not treat the presence of logs as proof that access is correct. Logs show what was recorded; authorization tests establish whether a request was permitted appropriately. Both are needed to investigate and improve the control.
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How do I prove that access can be revoked?
Exercise revocation rather than relying on a documented kill switch. Disable the agent, invalidate its tokens, rotate its credentials, remove stale grants, and confirm that downstream access actually stops.
- Record the identities, credentials, grants, and downstream sessions that the agent can use.
- Disable the agent or its ability to initiate new work, then invalidate tokens and rotate credentials as needed.
- Remove stale or unneeded grants, including those held by subordinate agents or integration identities.
- Attempt representative tool calls again and verify that the downstream system denies access.
- Record the elapsed time to revoke access and the recovery steps needed to restore an approved configuration.
Repeat the audit when a tool, workflow, data scope, model or runtime, tenant, or deployment environment changes. These changes can alter the effective permission chain even if the agent’s visible purpose remains the same.
What should I do if I find excessive or unclear access?
Contain the capability at the layer that grants it, then retest the task that still needs to work. Removing an access path may break a workflow; that is a reason to identify the minimum required permission, not to preserve a broad grant without understanding it.
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- If a tool accepts an unrestricted target or operation, constrain its schema and enforce the limit downstream.
- If a backend credential bypasses user authorization, replace that path with delegated authorization or a properly bounded workload identity.
- If logs cannot join the request to the downstream result, add correlation and authorization evidence before relying on them for incident investigation.
- If revocation does not stop access, identify remaining tokens, alternate identities, cached credentials, or downstream grants and test again after removal.
For unresolved access paths, document the capability, affected resources, accountable owner, compensating control, and approval needed to accept the risk. Avoid describing an untested path as safe simply because it has not yet produced a known incident.
How can I compare agent implementations?
Compare implementations by the boundaries they enforce and the evidence they produce, not by the identity label or the number of tools listed in a configuration. The table below is an audit framework, not a ranking of particular products.
| Comparison area | Stronger evidence | Warning sign |
|---|---|---|
| Identity model | Each call has a documented delegated or workload identity, with a named owner and bounded authority. | Calls inherit an individual developer’s credential or switch identities without an accountable reason. |
| Scope | Permissions are limited by tenant, resource, record, field, operation, and time where applicable. | A broad role is justified only by the agent’s general purpose. |
| Tool authorization | Allowlisted operations, validated typed arguments, separate read/write access, and backend enforcement. | Restrictions exist only in prompts or UI configuration. |
| Human control | Fresh approval is bound to the exact high-impact action and target. | One generic or reusable approval covers changed parameters or later actions. |
| Observability | Correlated records connect the initiating request, decision, tool, downstream check, and outcome. | Logs exist but cannot attribute a downstream change to a user request. |
| Containment | Token invalidation and access removal are exercised, with measured revocation time and recovery steps. | A kill switch is described but downstream access has not been tested after activation. |
| Deployment boundary | Tenant and guest boundaries are explicit and tested. | Cross-tenant paths are undocumented or assumed to be blocked by the interface. |
Frequently Asked Questions
Can I audit an AI agent without access to its source code?
You can still inspect identity assignments, tool configurations, API permissions, network destinations, and downstream logs, then run authorized black-box tests. Without source or runtime visibility, however, you may not be able to verify hidden tool paths or internal credential handling; record that limitation and avoid claiming a complete audit.
How often should an AI agent permissions audit be repeated?
Repeat it after changes to tools, workflows, data scope, model or runtime, tenant, or deployment environment. Organizations may also schedule reviews based on the sensitivity of the data and impact of the actions, but the available guidance does not establish a universal interval.
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Can an agent’s prompt prevent unauthorized data access?
A prompt can guide behavior, but it cannot enforce access control because a tool or downstream service may still receive a call with excessive authority. Enforce limits through identity scope, backend authorization, and validation of each requested target and operation.
What if an agent needs different permissions for different users?
Use a model that carries each user’s delegated authorization through the relevant tool calls, or separate workloads with explicitly bounded scopes where delegation is not appropriate. Test both users who should be allowed and users who should be denied; a shared broad identity can erase the distinction.
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