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To audit an AI agent safely, preserve its trace, compare every tool call with the target system’s own records, map the credentials and policies that enabled the action, then revoke access at each layer. A single toggle rarely does all of that. An agent may have a workspace role, enabled tools, an app-level permission, a connected account, and provider-issued OAuth scopes or API credentials. Stopping a run, disabling a tool, disconnecting an account, and revoking provider authorization are separate controls. This guide gives you a platform-aware process for investigating activity, reducing capability before execution, containing an active session, and proving that access was actually removed.
What does an AI agent’s “permission” include?
Permission is not one setting. It is the complete path from a person or service account to an agent, from the agent to a tool or app action, and from that tool to an external account or system. A change at one point in the path can leave another authorization intact.
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| Layer | What it controls | Typical audit question |
|---|---|---|
| Workspace or role access | Who can use the app, agent, or integration | Which users, groups, or service accounts were allowed to invoke it? |
| Enabled tool or app action | Which operations the agent can request, such as reading, writing, sending, deleting, or changing settings | Was the relevant action enabled when the run started? |
| Permission or approval policy | Whether a call runs automatically, pauses for approval, or is evaluated by a server-side policy | Did a person approve this call, or did policy allow it without a checkpoint? |
| Connected account | Which external account the app or agent uses | Which account, tenant, mailbox, repository, or workspace was connected? |
| Provider authorization | OAuth grants, scopes, API keys, delegated credentials, or managed-account authorization issued by the external provider | Does the provider still recognize the grant or credential? |
| Active session | The configuration and credentials available to a run already in progress | Could an existing session continue using an older policy? |
For a custom Model Context Protocol (MCP) server, add another checkpoint: identify who operates the server, the tools it exposes, the credentials it uses, 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 do I audit what an AI agent did?
Start with the platform trace, but do not treat it as a complete forensic record. A useful audit combines the agent’s own event data with records from the application it touched, the identity provider that authenticated it, and the infrastructure carrying the request.
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1. Preserve the investigation window
Write down the agent or session identifier, the start and end times in a stated time zone, the initiating user or service account, the connected provider, and the tool set that was available. Preserve the original export before changing configuration. Record who collected it and where the copy is stored so later reviewers can distinguish original evidence from analyst notes.
If the platform supports export, use the dashboard or API export function with the required read and export permissions. OpenAI’s Agents API tracing model separates a session into turns and a trace into the steps within a turn. Tool spans can include the call, arguments, result when available, status, duration, and other recorded data. Export the narrowest time range that answers the question, then retain the surrounding events needed to establish what happened immediately before and after the suspicious call.
2. Inspect at three levels
- Session: establish which agent instance, user, connected account, and policy context were active.
- Turn: identify the user or system instruction that led to a decision and the sequence of model responses.
- Tool call: inspect the exact tool name, arguments, approval state, result, status, duration, and any recorded error or denial.
Looking only at a final answer can hide a destructive intermediate call. Looking only at a tool name can hide the target selected by its arguments. Preserve both the requested operation and the returned result when the platform records them.
3. Correlate the trace with system-side evidence
An agent trace shows what the platform recorded about the run; it does not prove the operator’s intent or capture every downstream effect. Compare timestamps and identifiers with the target application’s audit events, identity-provider sign-in and OAuth records, and infrastructure logs.
| Evidence source | What it can establish | What to reconcile |
|---|---|---|
| Agent trace | Prompts or inputs, model outputs, tool calls, arguments, approvals, results, status, and duration when recorded | Whether the call was requested, approved, denied, or completed in the agent runtime |
| Target application audit log | Whether the external service accepted a request and what object or account it changed | Object IDs, actor identity, server timestamp, and resulting state |
| Identity-provider records | Sign-ins, token issuance, OAuth consent, scope changes, and account activity when logged | Which principal authenticated and which grant or credential was used |
| Infrastructure and network logs | Process, file, network, proxy, or server activity around the call | Source host, destination, allow or deny decision, and timing |
OpenAI describes agent-aware Codex telemetry as complementary to conventional security logs: conventional records can show process, file, or network activity, while agent-aware telemetry can capture prompts, approval decisions, tool results, MCP use, and network-proxy allow or deny events. Correlation is therefore stronger than either log source alone.
4. Build a timeline, not just a call list
For each consequential action, record the initiating event, the policy decision, the tool request, the provider response, and the observed state change. Mark gaps explicitly: for example, a trace may show a successful API response while the target application has no matching audit event. That discrepancy is a reason to investigate retries, asynchronous jobs, alternate credentials, clock differences, or an incomplete export—not a reason to assume the action did or did not occur.
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How do I map the capability path that enabled a call?
Draw the authorization chain before revoking anything. The chain should answer four questions: who was allowed to invoke the agent, which action was enabled, which account or credential did the action use, and which provider scope authorized the final request.
- Identify the principal. Determine whether the run began with a human user, workspace role, service account, or another automation.
- Identify the agent and session. Capture the agent version or configuration, session ID, policy state, and creation time.
- List enabled tools and actions. Separate read, search, write, send, delete, account-change, and financial operations rather than treating an app as one undifferentiated capability.
- Inspect the approval policy. Note whether the call required a person, was automatically allowed, or was evaluated by a server-side rule.
- Identify the connection. Record the external account, tenant, repository, mailbox, or other resource selected by the tool.
- Identify the credential. Determine whether the connection uses OAuth, a delegated credential, an API key, or a managed account, and find the provider-side record for it.
- Check the external scope. Compare the provider’s granted scopes or role with the operation recorded in the trace.
In ChatGPT’s documented administration model, role access, enabled actions, permission prompts, provider approval, and OAuth scopes are separate checks. A user may be allowed to use an app while a specific action is disabled, or an action may be disabled while an earlier provider grant remains active.
How should I reduce an agent’s capabilities before it runs?
Constrain the agent before execution. Give it only the tools and account connections needed for the task, and put a human checkpoint in front of operations whose effects are difficult to undo.
Remove unused tools and actions
- Disable tools that are not required for the current workflow.
- Separate read-only access from create, modify, send, delete, permission-change, and financial actions.
- Limit connected accounts to the specific tenant, project, mailbox, or repository needed.
- Review custom MCP servers for ownership, exposed tools, credential handling, and write capability before publishing them.
- Monitor use after enabling a new integration and stop a task that behaves unexpectedly.
OpenAI’s ChatGPT agent guidance recommends enabling only needed apps, monitoring use, and stopping suspicious tasks. It also cautions that these measures reduce harmful or unintended outcomes but do not eliminate every risk.
Choose an approval policy that matches the consequence
Anthropic’s Claude Managed Agents documentation distinguishes three policy choices:
| Policy | Behavior | Use when |
|---|---|---|
always_allow |
The tool call runs automatically | The operation is low impact and readily reversible |
always_ask |
The run pauses for a person before the call executes | A person must review every call before a consequential operation |
auto |
The server evaluates the call and may run, deny, or pause it | You accept that a call judged safe can execute without a human seeing it first |
The Claude documentation is explicit that auto is not a human checkpoint. If a person must review a tool’s calls before they run, configure always_ask for that tool. Apply this rule to messages, deletions, account changes, permission changes, and financial actions unless your risk assessment permits automation.
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How can I revoke an AI agent’s access during an incident?
Contain the active work first, then remove the capability, disconnect the account, and revoke the provider-side authorization or credential. Perform the actions in that order when possible, because a configuration change may not affect a session that is already running.
- Stop or contain active sessions. Use the platform’s stop, cancel, suspend, or equivalent session control. Preserve the session ID and current trace before termination if the platform allows it.
- Disable the relevant tool or app action. Remove the operation from the agent so a new run cannot request it.
- Disconnect the connected account. Remove the app’s connection to the external account or workspace.
- Revoke provider authorization. At the provider, revoke the OAuth grant, delegated credential, API key, or managed-account authorization used by the connection. If the provider offers a separate unlink control, use it as well.
- Reduce workspace access. Remove unnecessary users, groups, roles, or service accounts that could reconnect the integration.
- Rotate exposed credentials. If a key or delegated credential may have been copied, logged, or disclosed, replace it at the provider rather than relying solely on an app toggle.
ChatGPT’s app-permission guidance makes the distinction unambiguous: “Changing an app permission does not disconnect the app or revoke access already granted to a provider account.” Changing the permission controls supported actions; disconnecting the account addresses the connection, and provider-side unlinking or revocation may be a separate step.
Claude Managed Agents documents a similar operational boundary: disabling a tool removes it from the agent, while a policy update does not change sessions that are already running. Treat active sessions as a separate containment problem.
How do I verify that revocation worked?
Verification should produce observable evidence, not just a saved setting. Check each layer independently and run a harmless negative test when your platform supports one.
- Session: the original run is stopped, expired, or unable to issue another call.
- Tool and action: the agent configuration no longer exposes the revoked operation.
- Connection: the connected account is disconnected in the agent platform.
- Provider: the provider shows the OAuth grant, delegated credential, API key, or managed authorization as revoked, deleted, or replaced.
- Workspace: users and service accounts that should no longer use the integration lack the required role or access.
- Logs: later attempts are denied or absent, and any denial identifies the control that blocked the request.
A negative test should use a non-destructive operation and a controlled account. For example, after disabling a write action, confirm that the agent cannot enumerate that action as available or submit it; do not test revocation by attempting an irreversible change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common audit and revocation mistakes?
Assuming a final answer represents all activity
A final response can omit intermediate searches, retries, tool calls, and failed attempts. Inspect the session, turn, and tool-call records instead.
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Reading an agent trace as proof of an external state change
A recorded “success” is evidence of what the runtime received, not necessarily proof of the target system’s durable state. Match it to the target application’s audit event and resulting object state.
Confusing approval policy with authorization
A policy can require a prompt without removing the underlying OAuth scope. Conversely, revoking a scope does not necessarily remove the tool from the agent’s configuration. Audit both controls.
Using auto as if it meant “ask me first”
Under Claude’s documented policy model, auto may execute a call before a person sees it. Use always_ask where review must precede execution.
Changing a saved policy while a session is active
Some managed-agent updates apply only to sessions created afterward. Stop or contain existing sessions instead of assuming a future configuration change will interrupt them.
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Disabling an app action may leave the connected account and provider-issued authorization intact. Complete the disconnect and provider-revocation steps, then verify the provider record.
Ignoring alternate credentials
An agent may have more than one connection or credential path. Search the configuration and provider records for API keys, delegated credentials, OAuth grants, and managed accounts before declaring access removed.
How should teams compare agent platforms for auditability?
Do not reduce the comparison to a single “has audit logs” checkbox. Evaluate the controls below for the exact framework, deployment mode, connected provider, and credential model you operate.
| Capability to compare | Questions to answer |
|---|---|
| Event granularity | Are session, turn, and tool-call records available, including arguments and results? |
| Export and permissions | Can an authorized operator export traces or event streams, and which role grants that ability? |
| Decision visibility | Are approvals, denials, policy decisions, retries, and errors recorded? |
| External execution evidence | Can the platform show provider responses, or must operators correlate with application and infrastructure logs? |
| Approval choices | Can each risky tool require a human checkpoint, and is automatic evaluation clearly distinguished from human approval? |
| Running-session behavior | Does a policy or tool change affect existing sessions or only sessions created afterward? |
| Credential revocation | Does disconnecting an account revoke provider authorization, or is provider-side action separate? |
| Workspace visibility | Can administrators see which users, roles, accounts, and tools are active? |
What should an operator keep as a repeatable runbook?
Use this short runbook for routine reviews and incidents. Adapt the labels to your platform, but keep the separation between evidence, capability, and revocation.
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- Preserve: export the trace or event stream and record collection time, time zone, and permissions used.
- Reconstruct: inspect session, turn, and tool-call details; include arguments, approvals, results, status, and duration where recorded.
- Correlate: match the timeline to target-application, identity-provider, and infrastructure events.
- Map: document workspace role, enabled action, approval policy, connected account, and provider credential or scope.
- Constrain: disable unnecessary tools, narrow connections, and require explicit approval for consequential operations.
- Contain: stop active sessions before changing saved policy when immediate interruption is required.
- Revoke: disable the action, disconnect the account, revoke or rotate provider credentials, and remove excess workspace access.
- Verify: confirm each control in its owning system and inspect subsequent logs for denied or absent calls.
- Document: retain the evidence, decisions, timestamps, and remaining uncertainty so another operator can reproduce the conclusion.
Frequently Asked Questions
Should I rotate an API key after revoking an agent?
Rotate or replace it when the key may have been exposed, copied into logs, or shared with more principals than intended. Revoking an app connection does not by itself prove that every copy of a credential has become unusable.
How can I tell whether an agent used OAuth or an API key?
Inspect the tool or connection configuration, then confirm the matching authorization or credential record at the provider. The agent trace may identify the account or operation without naming the secret type, so provider-side records are often needed.
How long should AI-agent audit records be retained?
There is no universal retention period in the platform guidance. Set a period that matches your incident-response, contractual, and regulatory obligations, and preserve a longer immutable copy when an investigation or dispute is open.
Can I export an agent trace without being an administrator?
Only if your platform role includes the required read and export permissions. If the dashboard or API rejects the export, ask a workspace administrator to perform it and record who supplied the resulting evidence.
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