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To audit an AI agent safely, preserve its session trace, inspect every turn and tool call, compare the trace with application, identity-provider, and infrastructure logs, then remove capabilities and revoke connected credentials at each separate access layer. A permission toggle alone may not stop a running session or invalidate an OAuth grant. During an incident, stop active work first, disable the tool or app action, disconnect the account, revoke provider-side authorization, and verify that subsequent calls are denied or absent.
This process applies across agent frameworks and deployments. The exact screens differ, but the control layers are consistent: workspace role, enabled tool or app action, approval policy, connected account, and provider-issued credential.
What does an AI-agent permission actually control?
“Permission” is an umbrella term. An agent may be allowed to use a tool, an app action, a workspace resource, a connected account, or a provider-issued OAuth token. These controls are related but not interchangeable.
| Layer | What it governs | Typical change | What it does not necessarily do |
|---|---|---|---|
| Workspace or role access | Who can use the agent, app, or integration | Remove a user or group from an allowed role | Revoke a token already issued to a connected account |
| Tool or app-action enablement | Which functions the agent can invoke | Disable a search, write, messaging, file, or administrative action | Stop a call already in progress or erase external side effects |
| Approval policy | Whether a call runs automatically, pauses, or is denied | Require a human checkpoint for consequential tools | Remove the underlying account’s authorization |
| Connected account | Which external identity the agent uses | Disconnect the account from the agent platform | Guarantee that the provider has invalidated every previously issued credential |
| Provider authorization or credential | Whether the external service accepts the token, OAuth grant, API key, or delegated session | Revoke the grant, rotate the key, or unlink the application at the provider | Remove the agent’s local tool configuration |
Map all five layers before deciding that access is gone. A platform can stop presenting a tool while an external OAuth grant remains valid, or it can disconnect an account while an already-running session retains a usable configuration.
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How do I audit what an AI agent did?
Start with evidence preservation, then reconstruct the activity from the agent trace and independent system records. Do not treat a model transcript as a complete forensic record.
1. Preserve the identifying details
- Record the agent, session, conversation, or job identifier.
- Capture the start and end time in a stated time zone.
- Note the initiating human, service account, workflow, or scheduled trigger.
- List the connected provider, account, workspace, and enabled tools at the time.
- Record the deployment or framework version and any policy file or configuration revision.
Export the platform trace or event stream if the product supports export and your role has the required read permission. Preserve the original export before filtering it, and record who obtained it and when. Keep a hash or immutable copy if the event may be used in an incident review.
2. Inspect the session, turn, and tool-call levels
OpenAI’s Agents API tracing documentation describes a hierarchy in which a session contains turns and a trace records the steps within a turn. At the tool-span level, the record can include the call name, arguments, result when available, status, duration, and other recorded data. Inspect each level instead of reading only the final answer.
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| Level | Questions to answer | Useful fields |
|---|---|---|
| Session | Who or what started the work, and which account or environment was involved? | Session ID, principal, timestamps, workspace, model or agent version |
| Turn | What instruction, event, or intermediate result caused the next action? | Input, output, turn status, timing, approval state |
| Tool call | Exactly which operation ran, with which parameters and result? | Tool name, arguments, returned data, status, duration, error, approval decision |
Save the raw arguments and results where policy allows. Redacting secrets for investigators is sensible, but keep a controlled record of what was redacted and why; otherwise a later reviewer cannot tell whether a missing value was never recorded or was removed during handling.
3. Build a chronological timeline
- Sort events by a single time standard, such as UTC, while retaining each system’s original timestamp.
- Mark human approvals, denials, retries, timeouts, and policy changes.
- Link each tool call to the turn or trigger that preceded it.
- Identify calls that returned an error but may still have caused a partial external effect.
- Separate model-generated text from confirmed system results.
A successful-looking response is not proof that the external operation completed. Conversely, a timeout is not proof that nothing happened; check the target system.
Why is an agent trace not a complete audit?
The trace shows what the agent platform recorded, not every effect, motive, or downstream event. Correlate it with the systems that accepted the request.
Application and data records
Check the target application’s audit history for object creation, edits, deletions, messages, file downloads, permission changes, and API requests. Match its request ID, actor, account, or timestamp to the tool span. If the application records a different actor name, document the identity mapping rather than assuming it is a separate person.
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Identity-provider records
Review sign-ins, token issuance, OAuth consent, scope changes, refresh-token use, multifactor events, and account unlinking. These records can show that a credential was used even when the agent platform did not retain the full response.
Infrastructure and network records
Use gateway, proxy, container, host, file-system, database, and network logs to confirm processes, destinations, and data movement. OpenAI’s agent-aware Codex telemetry guidance describes such telemetry as complementary to conventional security logs: traditional records show process, file, or network activity, while agent-aware records can include prompts, approval decisions, tool results, MCP use, and network allow or deny events.
Correlation is an operational safeguard, not a claim that any one log is authoritative. Clock skew, retries, asynchronous jobs, and intermediary services can create several records for one action.
How do I map the agent’s capability path?
Draw the path from the person or service that authorized work to the external account that ultimately accepted it. This exposes controls that a single dashboard view can hide.
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- Principal: identify the human, service account, workflow, or scheduler that initiated the run.
- Workspace: determine which organization, project, tenant, or environment supplied the agent.
- Agent policy: list enabled tools, app actions, model instructions, allowlists, and approval rules.
- Connector: identify the connected account, MCP server, API gateway, or integration endpoint.
- Credential: record whether access uses OAuth, an API key, a delegated session, a managed account, or another token.
- Target: name the external application and the data or operation the credential can reach.
For a custom MCP server, document who operates it, which tools it exposes, what credentials it holds, and whether write or modification operations are enabled. OpenAI advises administrators to vet and test custom MCP connections before publishing them and warns that an untrusted server can increase prompt-injection risk.
How should I reduce an agent’s permissions before it runs?
Use least privilege and put a human checkpoint in front of actions that are difficult to undo. Restricting capability before execution is safer than trying to reconstruct damage afterward.
Choose only the tools the task needs
- Disable unused apps, tools, data sources, and write actions.
- Limit file, repository, mailbox, calendar, and database scope to the smallest useful set.
- Use separate read-only and write-capable connectors where the platform supports them.
- Prefer short-lived or task-specific credentials over a broad, permanent token.
- Constrain network destinations and MCP tools with explicit allowlists.
Set an approval policy that matches the consequence
OpenAI’s ChatGPT agent guidance recommends enabling only needed apps, monitoring use, and stopping suspicious tasks. Anthropic’s Claude Managed Agents documentation distinguishes three policy modes:
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| Mode | Behavior | When it fits |
|---|---|---|
always_allow |
The call runs automatically. | Low-impact, reversible reads or routine operations with a well-understood scope. |
always_ask |
The call pauses for a person’s approval before execution. | Messages, deletions, account changes, financial actions, or other consequential writes. |
auto |
The server evaluates the call and may run, deny, or pause it. | Situations where server-side policy is trusted and a human checkpoint is not required for every call. |
The Claude documentation explicitly says that auto is not a human checkpoint: a call judged safe can execute before anyone sees it. If a person must review a tool call before it runs, configure always_ask for that tool.
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Contain active work first, then revoke each layer in order. Do not rely on a saved policy change to stop a session that is already executing.
- Stop active work. Cancel or disable the running session, job, workflow, or agent process using the platform’s emergency control. If no kill switch exists, suspend the initiating service account or block the connector at the network or gateway layer.
- Disable the tool or app action. Remove the specific write, messaging, file, administrative, or financial capability. Record the previous configuration for the incident timeline.
- Disconnect the connected account. Remove the account from the agent platform so future calls cannot use that connection.
- Revoke provider authorization. At the external provider, revoke the OAuth grant, rotate or disable the API key, invalidate delegated sessions, or unlink the application when those controls are available.
- Remove workspace access. Revoke the agent, user, group, or service account’s role if the incident requires broader containment.
- Preserve evidence. Export traces and relevant provider logs before retention windows or account deletion removes them.
These actions address different controls. OpenAI’s Help Center states: “Changing an app permission does not disconnect the app or revoke access already granted to a provider account.” Disconnect the account, and use the provider’s own unlink or revoke control when available.
Does disabling an app revoke its OAuth access?
No. Disabling an app permission generally changes when supported actions can run; it does not by itself disconnect the account or revoke authorization already granted to the provider. Disconnect the account in the agent platform and then verify or revoke the grant at the provider.
Also check refresh tokens and existing sessions. A provider may continue honoring a token until it expires or is explicitly revoked, even if the app no longer displays the action.
What happens to sessions after I change a policy?
Configuration changes may apply only to newly created sessions. Claude Managed Agents documentation says updates apply to sessions created afterward, so an active session must be handled separately during containment.
After changing a policy, identify every running session, queued job, retry worker, and scheduled trigger. Stop or drain them, then start a new session under the corrected policy. Test with a harmless operation and inspect the trace to confirm that the expected approval or denial appears.
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How do I verify that revocation worked?
Verification should test the agent, the account, and the provider rather than relying on a single status label.
- Session state: confirm that active work is stopped, cancelled, or unable to create new tool calls.
- Tool availability: inspect the agent configuration and confirm that the relevant action is disabled or absent.
- Account state: verify that the connected account is disconnected and that no replacement connection points to the same identity.
- Provider state: check OAuth grants, API keys, delegated sessions, and sign-in records for revocation or rotation.
- Negative test: attempt a controlled call and confirm a denial, missing tool, invalid credential, or equivalent failure without causing a real side effect.
- Subsequent logs: monitor agent, application, identity-provider, and infrastructure events for denied or absent calls.
Keep the verification timestamp and the identity that performed it. If a token remains valid after local disconnection, escalate to provider-side revocation and rotate any secret that the agent could have exposed.
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| Control to compare | Questions to ask |
|---|---|
| Event granularity | Can you inspect session, turn, and individual tool-call arguments, results, status, and duration? |
| Export and access | Can authorized operators export raw events, and are exports protected from alteration? |
| Approval evidence | Are approvals, denials, retries, and policy decisions recorded with the call? |
| External execution | Does the trace show only the attempted call, or can it be correlated with the target system’s confirmed result? |
| Policy semantics | Does a mode pause before execution, or can an automated evaluator run a call first? |
| Session behavior | Do policy changes affect running sessions, queued work, and retries? |
| Credential revocation | Does disconnecting an account revoke provider authorization, or is a separate provider action required? |
| Administrative visibility | Which roles can view traces, change tools, disconnect accounts, and revoke credentials? |
OpenAI Agents API tracing
The tracing model provides session, turn, and tool-span views, with dashboard inspection and API export when tracing is enabled and the operator has the necessary permissions. Use it to reconstruct the agent’s sequence, then correlate the sequence with application and identity records.
ChatGPT app administration
ChatGPT’s documented administration model separates role access, enabled actions, permission prompts, and provider authorization. Changing an action or permission prompt does not substitute for disconnecting the account or revoking the provider grant.
Claude Managed Agents
Claude’s policy modes distinguish automatic execution, human approval, and server evaluation. Tool removal and policy updates are separate operations, and a saved update may not alter sessions that are already running.
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What are the most common audit and revocation mistakes?
Relying on the final answer
A final response can omit failed calls, retries, hidden intermediate steps, or actions performed asynchronously. Review the trace and target-system events instead.
Assuming “disabled” means “revoked”
A disabled app action may leave an OAuth grant, API key, refresh token, or active session untouched. Disconnect locally and revoke at the provider.
Changing policy while work is running
A configuration update may affect only future sessions. Enumerate and stop current sessions, queued jobs, and retries.
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Skipping negative testing
A green status badge does not prove that an old token is unusable. Attempt a safe, controlled call and confirm the expected denial without touching production data.
Failing to preserve context
Without the initiating principal, time zone, configuration revision, and provider account, investigators may be unable to connect an agent event to the external effect.
Operational checklist
- Identify the session, principal, provider, workspace, tools, and time window.
- Export the raw trace and preserve its original form.
- Inspect session, turn, and tool-call records, including arguments, results, status, and approvals.
- Correlate with application, identity-provider, network, host, and data-system logs.
- Map workspace roles, tool enablement, approval policy, account connection, and provider credential.
- Disable unnecessary tools and require explicit approval for consequential actions.
- Stop active sessions and queued work before relying on a policy change.
- Disconnect the account and revoke or rotate provider credentials.
- Run a controlled negative test and monitor for denied or absent calls.
- Record who performed each change, when, and which configuration revision is now active.
Frequently Asked Questions
Can an AI agent delete data after I revoke its permission?
Revocation stops or limits future access; it cannot undo an operation that already completed. Check the target system’s audit trail, backups, recycle bin, or transaction history and follow that system’s recovery procedure.
How long should agent action logs be retained?
Use the retention period required by your security, privacy, and regulatory policies, and preserve an incident-specific export before normal rotation removes it. Retention should cover delayed jobs, provider records, and the time needed to investigate disputed actions.
What should I do if the agent used an API key instead of OAuth?
Disable or rotate the key at the service that issued it, then remove it from the agent, secret store, CI variables, and logs where it may have been copied. Review key-use records to identify calls made before rotation.
Can I audit an agent that has no built-in tracing?
Collect the surrounding evidence: application audit events, identity-provider sign-ins and token records, gateway or proxy logs, host activity, and workflow history. Add a correlation ID at the orchestration layer so future tool calls can be tied to one run.
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