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Give each AI agent its own identity, grant that identity only the permissions and data access its task requires, and verify what it does with Cloud Audit Logs. For Google Cloud-native runtimes that support it, Agent Identity is the preferred option; otherwise, use a dedicated service account for one agent and its lifecycle. The audit is not complete until you have checked effective permissions, tested both allowed and denied access, and confirmed that the relevant Data Access logs are enabled.
What does it mean to audit an AI agent’s access?
An access audit answers two different questions: what could the agent access, and what did it actually access? The first is determined by its identity, direct and inherited IAM grants, delegation paths, and applicable boundaries. The second is evidenced by logs, but only for services and activity types that are logged and configured for the data you care about.
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That distinction matters because a narrow-looking project-level role may coexist with inherited access, group membership, service-account impersonation, or user-delegated credentials. Conversely, a configured permission does not prove the agent used it. A useful audit compares the declared authority—the tasks and data the agent is supposed to handle—with its effective permissions and observed API activity.
Cloud Audit Logs are designed to record who did what, where, and when. Google’s Cloud Audit Logs overview explains the log types and their behavior. Logs can provide evidence of Google Cloud resource access, but they do not by themselves establish what the model inferred from retrieved content or whether information was later exposed in an answer.
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What should I define before reviewing permissions?
Write down the agent’s authority before changing IAM. This gives you a concrete basis for deciding which grants are necessary and for testing whether the final configuration works without opening unrelated data.
Build an authority record
- Tasks: State what the agent is meant to do, such as retrieve specified information or perform an approved change.
- Operations: Separate read-only retrieval from creating, updating, deleting, or administering resources. Do not treat “the agent needs access” as a single permission.
- Resources and destinations: Identify the projects, buckets, datasets, APIs, and other destinations it needs, including relevant cross-project access.
- Data classifications: Record which information is sensitive and which locations must remain inaccessible.
- Runtime and tools: Record where the agent runs and which tools or connectors can make requests on its behalf.
- Delegation: Specify whether the agent acts only as itself or can act for a user, and how the human identity is retained in the audit trail.
- Lifecycle: Identify who owns the agent and what should happen to its access when its purpose, runtime, or owner changes.
Use this record as the scope for the inventory, access policy, test plan, and future reviews. If a new tool, prompt-driven capability, runtime, or data store changes what the agent can do, treat that change as an access-review trigger.
Which identity should an AI agent use?
Prefer a distinct identity for each agent. Use Agent Identity where the selected runtime supports it; otherwise, assign a dedicated service account to one agent and one lifecycle. For an agent acting on behalf of a user, evaluate managed user-delegated OAuth separately rather than passing raw user credentials to the agent.
| Design | Strengths | Limits and checks |
|---|---|---|
| Agent Identity | Provides a per-agent cryptographic identity with strong attestation, agent and user attribution, and integration with IAM, Principal Access Boundary, and VPC Service Controls. Google documents that agent identities are not shared by multiple workloads by default, cannot be impersonated, and do not permit developers to create long-lived service-account keys. | Confirm that the runtime and region support the required features. Google describes some limitations and an evolving product status, so confirm current applicability before relying on a specific control. See Agent Identity overview. |
| Dedicated service account | Works with a broad range of Google Cloud runtimes and familiar IAM controls. Grants can be narrowed with custom roles, conditions, downscoped tokens, and short-lived credentials. | Use a separate account for agents with different lifecycles or data needs. Shared accounts obscure which application acted and can accumulate privileges as functionality expands. Control key creation and impersonation paths strictly. |
| User-delegated OAuth through an auth manager | Can support actions on behalf of a user while keeping credentials managed and attribution available. | Check scopes, consent, revocation, token lifetime, and safeguards against exposing raw credentials. Confirm that logs and the surrounding design preserve both user and agent attribution. |
No identity design substitutes for least privilege. Agent Identity can improve identity separation and attestation, but the agent still needs only the IAM permissions required for its task. A service account can also be appropriately constrained, but its key, token, and impersonation paths need particular attention.
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How do I inventory the agent’s effective permissions?
Use Cloud Asset Inventory and IAM policy analysis to identify direct and inherited grants, then follow the paths through which the agent could obtain or use credentials. Record the intended access and the effective access separately; a role attached directly to the agent is only one part of the picture.
- Identify the principal. Record the agent’s identity and the runtime that uses it. If the agent delegates to a user or another principal, include that path in the inventory.
- Trace IAM bindings. Check grants at the resource, project, folder, and organization levels, including inherited policies and group memberships. Note the resource each grant applies to and whether a condition limits it.
- Inspect credential paths. Look for service-account impersonation, token generation, OAuth delegation, and other ways the agent could act with a different identity. A well-scoped direct grant does not eliminate risk from a broader credential path.
- Map data destinations. Include the storage locations and services the agent can reach, not just the project where its runtime runs. Record cross-project access and sensitive resources that must be denied.
- Compare grants with the authority record. For every permission or access path, document the task that requires it. Flag access with no clear task justification for removal or investigation.
- Preserve a baseline. Keep a record of the principal, grants, resource scope, delegation paths, and review date so later changes can be compared with the intended configuration.
Review group grants and inherited permissions as carefully as direct bindings. A principal may receive access through a group or higher-level policy even when no obvious grant appears on an individual bucket or dataset. Likewise, account for capabilities that let the agent obtain another identity’s token; otherwise, the inventory can understate its real authority.
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Grant the smallest permission set that supports the declared tasks, at the narrowest resource scope available. Keep reading separate from modifying or administering, and do not use broad Owner or Editor grants as a shortcut for making an agent work.
Reduce the grants
- Prefer a narrow predefined role when it covers the required operations; use a custom role when the agent needs a smaller set of permissions.
- Scope bindings to the specific resource or project needed rather than granting access across a larger hierarchy without a task-based reason.
- Use conditional bindings where an additional condition can meaningfully limit when or where a grant applies.
- Separate agents with different data needs or lifecycles instead of sharing a service account. Shared identities can collect permissions over time and make activity harder to attribute to a particular agent.
- Where appropriate, use temporary elevation, short-lived credentials, and credential downscoping rather than standing broad access.
- Remove unused grants and review the resulting effective permissions, including inherited and delegated access, after changes.
Add boundaries and credential protections
IAM allow and deny policies, conditional bindings, Principal Access Boundary (PAB) policies where supported, and VPC Service Controls can add layers beyond an individual role grant. These controls address different parts of the access problem: IAM determines which permissions are granted or denied, a PAB can constrain principal access where supported, and a service perimeter can help protect resources within its boundary. Confirm that each control applies to the chosen identity, resource, and runtime rather than assuming that adding a policy automatically covers every path.
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For delegated user access, keep the agent from obtaining or exposing raw end-user credentials. Manage consent, scopes, and revocation through the delegated authorization design, and preserve attribution to both the user and the agent. Review token generation and impersonation paths alongside the direct resource grants; otherwise, a narrow role may be bypassed by a credential the agent can acquire.
Which Cloud Audit Logs should I enable?
Admin Activity logs are always written and cannot be disabled, and Policy Denied logs are generated by default. Data Access logs are disabled by default for many services, with BigQuery as an exception, so explicitly configure the services whose data access must be auditable.
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Google’s Cloud Audit Logs documentation describes the log categories and configuration. It also notes that Data Access logging may create additional log-usage charges. Treat log coverage as part of the access design: a permission review cannot prove which data was accessed if the relevant service does not produce the needed Data Access records or those records are not retained where reviewers can examine them.
Configure and protect the evidence
- Select the services and events. Determine which services hold the sensitive data or expose the agent’s tools. Enable the needed Data Access logging in the IAM AuditConfig for those services. Google Cloud MCP servers write service-specific Data Access logs; their logging can be enabled through the
mcp.googleapis.comIAM AuditConfig. - Route the logs. Send relevant records to a protected logging bucket or analytics destination that security reviewers can access independently of the agent.
- Set retention deliberately. Choose retention that meets your operational and audit needs. The applicable duration depends on your requirements; do not assume that the default retention is sufficient.
- Restrict log access. Limit who can read the audit evidence. Use Logs Viewer or Private Logs Viewer as appropriate, and avoid giving the agent itself a way to alter or suppress the records used to evaluate its activity.
- Verify records arrive. After configuration, perform representative allowed and denied actions in a non-production project and confirm that the expected records appear in the destination.
Admin Activity records and Data Access records answer different questions. Administrative changes can be captured even when data-read auditing is not enabled for a service. If the goal is to determine whether an agent read a sensitive resource, verify Data Access coverage for that service rather than assuming an always-on administrative log will show the read.
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How do I test what the agent can and cannot access?
Test the configured identity in a non-production project before relying on it. A useful test plan checks expected behavior, prohibited behavior, and the evidence trail—not just whether the agent’s main workflow succeeds.
Run a permission test matrix
- Expected reads: Verify each required read against the intended resource and identity.
- Denied resources: Attempt access to sensitive buckets, datasets, or projects that are outside the agent’s authority; confirm the attempt is denied.
- Mutation attempts: Try write, update, and delete operations if the agent is intended to be read-only. Confirm that it cannot make changes through an alternate tool or identity.
- Cross-project access: Check both the required cross-project path, if any, and a project the agent should not reach.
- Credential escalation paths: Test whether the agent can impersonate a service account, generate a token, or otherwise obtain a broader identity than intended.
- Emergency revocation: Exercise the procedure for removing access and verify that the agent can no longer use the affected permission or credential path.
- Audit evidence: Confirm that allowed access and relevant denials generate records in the protected destination and can be attributed to the intended principal.
Keep the result of each test tied to the identity, resource, and operation it covered. A successful read test on one dataset does not establish access behavior on another service, and a denial test does not establish that every alternate credential path is closed.
Monitor for changes and exceptions
Create log-based metrics or alerts for unexpected principals, sensitive-resource reads, policy denials, privilege changes, and anomalous token activity. An alert is most useful when it points to the identity, resource, and event that should be reviewed, rather than treating all activity as equally suspicious.
Compare observed API activity with the agent’s declared authority. Investigate calls that have no task-based explanation, and review whether denials indicate an attempted boundary crossing or simply an incomplete grant for an approved task. Re-run the inventory and relevant tests after role changes, tool additions, prompt changes that alter capabilities, runtime migrations, or new data stores.
How should I keep the audit current?
Make the access review a recurring operational control, not a one-time setup task. The agent’s permissions can become broader as its capabilities expand, and an old access inventory can stop reflecting the runtime or data it actually uses.
- Schedule periodic reviews of direct grants, inherited policies, group membership, and credential paths.
- Remove unused permissions and verify the effective result after each cleanup.
- Review delegated credentials and revoke or rotate them when the authorization or lifecycle changes.
- Compare observed API calls with the authority record and document exceptions that have a valid task reason.
- Reassess access when an agent gains a tool, changes runtime, takes on new tasks, or connects to a new data store.
- Confirm that audit log configuration, routing, retention, and reader permissions still meet the evidence requirement.
The outcome should be a traceable link between a task, a distinct agent identity, the smallest necessary grants, the data destinations it may reach, and logs that let a reviewer investigate activity. If any part of that chain is missing, the audit is incomplete.
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Frequently Asked Questions
Can Cloud Audit Logs prove that an AI model understood or retained data it accessed?
No. Audit records can document Google Cloud activity such as access to a resource, but they do not establish what the model inferred, retained internally, or disclosed in a later response. Pair cloud access evidence with the controls and review processes used for the agent’s outputs and data handling.
What should I do if the agent’s runtime does not support Agent Identity?
Use a dedicated service account for that agent and lifecycle, then constrain its roles, resource scope, credential lifetime, and impersonation paths. Reassess identity support when the runtime changes rather than assuming the service-account design is permanent.
Why might a sensitive-data read be missing from my audit evidence?
A common reason is that Data Access logging was not enabled for the service that holds the resource; it is disabled by default for many services. Also check that the relevant records are routed to a destination with suitable retention and that the reviewer can read them.
How should I respond when an access-denied alert fires?
First establish which principal, resource, and operation were denied, then compare the event with the agent’s declared tasks. If it was an expected task, adjust only the narrow missing permission after validating the scope; if it was outside the declared authority, investigate the tool or credential path that initiated it.
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