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You can let an AI agent do useful work without giving it your full account authority. Give it a separate identity, limit that identity to the specific data and actions the task needs, use access that expires, and enforce permissions outside the model. Start with read-only access; require fresh human approval for consequential changes; and keep logs and a tested way to revoke access. The right identity pattern depends on whether the agent is acting on behalf of a signed-in user or running unattended.
Why is broad account access risky?
An AI agent can use tools to read information or take actions, so the authority behind those tools matters as much as the instructions given to the model. If an agent can access an entire account, a mistake or manipulated response may affect more data and operations than the task requires. Content from a website, email, or document can also influence an agent; a prompt telling it to behave safely is not a security boundary.
That is why security guidance emphasizes least privilege, isolation, action-level controls, and human oversight. The Canadian Centre for Cyber Security recommends scoped entitlements, ephemeral credentials, sandboxing, and revocation in its careful-adoption guidance. OWASP likewise recommends controls that limit capabilities and validate actions outside the agent itself in its general controls.
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These measures reduce the consequences of an error or manipulation; they do not prove that an agent is safe. Choose controls based on what the agent can actually reach and change, rather than on how reassuring its prompt or explanation sounds.
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Why a prompt cannot provide authorization
A system message can tell an agent not to delete files, but it cannot prevent a tool call that the surrounding application accepts. Authorization has to be checked in the API, application, operating system, or policy engine before each action. The check should use the active identity, the requested operation, and the specific target. If any of those facts cannot be verified, the safe result is to refuse the call.
This distinction also matters when external content is involved. An email, web page, document, or tool description may contain instructions that conflict with the task. Treat that material as untrusted input. A narrow identity and a restricted runtime limit what an influenced agent can do, even when its reasoning appears confident.
Which identity should an AI agent use?
Use delegated user access when the agent is doing work for a signed-in user with that user’s data. Use a dedicated workload identity when an automation runs without a user present. Neither pattern should inherit unrestricted authority.
| Pattern | Best fit | How authority is tied to the work | Key safeguards |
|---|---|---|---|
| Delegated user access | A user-directed task involving data that user is permitted to access. | The user signs in and requests the action; the downstream service enforces the user’s permissions. | Limit scopes and operations to the task, check each action, and retain attribution to the initiating user. Delegation is not permission to inherit every account capability. |
| Dedicated app-only or workload identity | Background automation that runs without a signed-in user. | A named service identity receives the permissions required for the defined automation. | Keep it out of administrative roles; assign a named owner; provide a disable, credential-rotation, token-invalidation, and stale-permission-removal path. |
Microsoft describes these as different access patterns: delegated access preserves the user’s permission context, while app-only access serves work that does not have a signed-in user. See Microsoft’s access patterns and controls and its guidance on identity, access, and least privilege. A backend identity should not become a way around the user’s actual access rights.
Delegation is not a blank check
Delegated access is appropriate when a person is asking the agent to work with information that person could already access. It keeps the downstream service aware of the user’s permission context and makes the action easier to attribute. Still, request only the scopes and operations the workflow needs. A user who can administer an account may be allowed to ask for a summary without authorizing the agent to administer the account. The service should evaluate every operation, not assume that a successful sign-in permits every later tool call.
Designing a workload identity
For unattended jobs, create a named app-only or workload identity rather than reusing an employee’s credentials. Record an owner and the automation’s purpose. Keep the identity out of administrative roles, give it only the resources and operations defined for the job, and document who can disable it. The operating procedure should cover credential rotation, token invalidation, and removal of permissions that become stale. An identity that nobody owns or can shut down is an operational risk even when its initial scope is narrow.
How should you scope an agent’s access?
Define the task in terms of the resources it may reach, the operations it may perform, and how long the access lasts. Grant only what is necessary for that task, and separate permission to read from permission to change data.
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Limit resources and operations
Specify the relevant account, project, files, records, or other resources rather than granting access to an entire account or workspace by default. Then decide which operations are needed. An agent asked to summarize documents may need to read those documents, but not delete them, send messages, or change account settings. Where a task truly needs a write operation, grant that operation narrowly instead of enabling general-purpose editing.
Write the scope in concrete terms before implementation: which tenant or workspace, which collections or folders, which tool, and which verbs. “Manage the database” is not a useful boundary; “read these records and update this one status field” is easier to test and review. If the task spans multiple resources, list each one and explain why. Remove an optional permission instead of keeping it for convenience.
Limit duration
Prefer credentials that are short-lived and tied to a task over long-lived credentials that remain usable indefinitely. If elevated permission is needed, require an explicit policy decision or human approval, grant it for the permitted work, and remove it afterward. Microsoft discusses short-lived, scoped tokens and approvals in its least-privilege guidance.
Duration should match the job. A one-time investigation should not leave a token active for a recurring workflow, and a scheduled process should renew only the permissions it still needs. Include expiration and revocation in the design, not as an emergency feature added later.
Start read-only
Begin with read-only access where possible. This lets you check whether the agent can find and interpret the necessary information before allowing it to modify anything. Add write capability only for a defined operation that the task needs; do not treat a successful read-only trial as a reason to grant broad write access. The U.S. Department of Energy’s GEAR guidance also recommends read-only starts and constrained network access in its AI security and safety guidance.
Use the read-only phase to identify unnecessary tools, unexpected data exposure, and ambiguous targets. Then enable one narrowly defined mutation at a time. Keep the read and write credentials or policy paths distinct where practical, so a task that only needs information cannot silently turn into a change operation.
What should the execution layer check before a tool call?
Authorize each tool call in the application, API, operating system, or policy layer before it runs. The model can propose an action, but it must not be the component that decides whether the action is permitted.
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- Use an allowlist. Make only the tools and operations needed for the task available. Deny unlisted actions by default.
- Check the active identity and scope. Confirm that the agent’s current credentials permit the requested operation on the specific resource, and, for delegated work, bind the call to the initiating user or session.
- Validate the target and parameters. Use narrowly typed inputs and check that the target resource and operation match the policy. Do not let an unchecked model-generated target silently determine what gets changed.
- Fail closed. If authorization cannot be verified, required approval is missing, or the request falls outside policy, do not execute the action.
- Record the decision and outcome. Keep an attributable record of the agent, effective permissions, action, target, and relevant user or organization context.
OWASP’s general controls call for backend enforcement and per-tool controls; the AI Agent Security Cheat Sheet describes approval and execution controls for high-impact actions. A system prompt can guide behavior, but it cannot replace these checks.
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It is useful to let an agent propose a plan or a structured tool request, but treat that output as untrusted input to an execution service. The service should translate the request into a narrowly typed operation, check the identity and policy, and then execute or reject it. Do not infer authorization from a natural-language explanation such as “the user asked me to do this.” Preserve the user or session identifier as structured context and check it at the point of action.
When should a person approve an agent’s action?
Require a fresh approval before actions with significant impact, and make that approval specific to the proposed action and target. Do not give the agent a standing, general-purpose approval that silently covers future consequences.
- Deleting or overwriting data
- Sending a message or other communication outside the organization
- Making a purchase or committing funds
- Deploying to a production environment
- Changing permissions
- Initiating account recovery
The execution component should verify both that the action is authorized and that the required approval covers that action. An approval is an additional control, not a substitute for scoped permissions or validation. OWASP’s security cheat sheet recommends approval and execution controls for high-impact operations.
Show the reviewer the exact operation, target, relevant parameters, and expected effect. Approval for “send email” should identify the recipients and content or a clearly bounded template; approval for a deployment should identify the target environment. If the agent changes the target or material parameters after approval, require a new decision. An approval workflow that is too broad to explain is probably too broad to be meaningful.
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Run the agent in a sandbox, development container, or disposable environment with only the files, processes, and network destinations required for its task. Keep production credentials and unrelated home-directory data out of its reach.
- Limit filesystem access to the working files the task needs; avoid mounting unrelated personal or organizational directories.
- Restrict network egress to destinations required for the task instead of granting unrestricted access.
- Keep production secrets out of the environment unless the task requires them and policy permits their use.
- Treat material retrieved from websites, emails, documents, and tool descriptions as untrusted input.
These steps matter because outside content may try to influence an agent that has access to tools or private data. OWASP’s 2025 Top 10 for LLM Applications addresses least privilege, high-risk approvals, separation of external content, and adversarial testing. Containment limits exposure if an agent is manipulated; it does not establish that manipulation cannot happen.
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Reduce the blast radius before adding tools
Every additional tool expands the set of actions that must be reviewed. Remove tools the workflow does not need, and expose only the relevant data fields where an API permits that level of filtering. A document summarizer may need document text but not a directory listing, account settings, or a message-sending tool. Network restrictions should be equally specific: permit the destinations needed to retrieve or submit task data and deny unrelated egress.
What should you log, review, and be able to revoke?
Keep enough information to determine which agent acted, what authority it used, what it did, and on whose behalf. Logs should make the effective scope visible, not merely record that an agent or tool was involved.
- Agent identity and, where applicable, the initiating user or responsible organization
- Effective permissions or scope at the time of the action
- Operation and target resource
- Authorization and approval outcome
- Action result, including failures
Maintain a practical way to disable the agent, rotate its credentials, invalidate tokens, and remove permissions it no longer needs. Test those steps rather than assuming they will work during an incident. Re-review access when the tools, workflow, data scope, or deployment conditions change. Microsoft’s access-pattern guidance discusses identity and permission management; the Canadian Centre for Cyber Security also covers revocation in its agentic AI adoption guidance.
Make revocation an exercise, not a document
Test the complete shutdown path: disable the workload identity or agent, rotate or revoke credentials, invalidate active tokens, and remove obsolete permissions. Confirm that already-running jobs cannot continue using cached authority beyond the policy’s intended lifetime. Repeat the test when the execution platform or identity provider changes. A control that exists only on paper may fail when it is most needed.
How do you choose a safer design for your use case?
Start with the work the agent must do, then compare designs by the authority they receive, what they can change, and how quickly you can stop them. No single identity pattern fits both user-directed work and unattended automation.
| Decision | Question to answer | Safer default |
|---|---|---|
| Authority source | Is a signed-in user directing the task, or must it run unattended? | Use delegated access for work on behalf of a user; use a dedicated workload identity for background work. |
| Scope | Which resources, tools, operations, and tenants are actually needed? | Allow only the necessary resources and operations; deny other access by default. |
| Mutation risk | Can the agent write, delete, send, spend, deploy, or change permissions? | Start read-only; grant narrow write access only where needed and gate consequential actions with approval. |
| Duration and revocation | How long is access needed, and can it be stopped quickly? | Prefer task-bound, short-lived access and test disabling, token invalidation, and permission removal. |
| Containment | Which files, secrets, processes, and network destinations can the agent reach? | Isolate the runtime and expose only what the task requires. |
| Oversight and traceability | Can a reviewer see who authorized an action and what happened? | Log the agent, effective scope, action, target, and user context; require specific approval for high-impact actions. |
For a user-directed task, preserving the user’s permission boundary is central. For unattended work, ownership and a reliable shutdown and credential-revocation path are essential. In either case, enforce access in the backend and review the whole path from identity to action. NIST’s February 2026 concept paper on software and AI agent identity and authorization frames identity, authorization, delegation, auditing, and prompt-injection mitigation as areas for design and further work; it is a concept paper, not a quantitative comparison of control effectiveness.
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- Describe the job. List the data sources, tools, operations, users, and expected outputs. Mark actions that can affect people, money, production systems, or permissions.
- Choose the authority source. Use delegated access for a signed-in user’s request or a named workload identity for unattended automation. Record ownership and accountability.
- Build the smallest policy. Allow only the necessary resources and operations. Separate read and write permissions, and set an expiration or task lifetime.
- Run in a contained environment. Use a sandbox or disposable runtime, limit filesystem mounts and network destinations, and keep unrelated secrets out.
- Test read-only behavior. Check data exposure, target selection, failure handling, and reactions to untrusted content before enabling mutations.
- Add approval gates. Require specific approval for deletion, external communication, spending, production deployment, permission changes, and account recovery.
- Instrument and rehearse. Verify logs contain identity, scope, action, target, approval, and result. Exercise disable, rotation, token invalidation, and permission removal.
- Review after change. Reassess access whenever tools, workflows, data scope, or deployment conditions change.
This sequence does not make the agent infallible. It makes authority explicit, limits the consequences of mistakes, and gives operators evidence and control when behavior or requirements change.
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Frequently Asked Questions
Can I use the same AI agent identity for several people?
A shared identity can make it difficult to tell which person’s authority or request led to an action. For user-directed work, keep calls linked to the initiating user and enforce that user’s permissions; for background work, assign the workload identity a named owner and keep its scope limited to the automation.
Should an AI agent receive an administrator role just to avoid permission errors?
No. A permission error is a signal to check whether the task needs a specific additional capability, not a reason to grant broad administrative authority. Add only the missing, justified permission and keep it limited in scope and duration.
Do approval gates make an AI agent safe from prompt injection?
No. Approval can prevent some consequential actions from proceeding without review, but it does not prevent an agent from being influenced by untrusted content or misreading a task. Combine approvals with backend authorization, narrow access, and isolation.
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Is delegated access always safer than an app-only identity?
No. They solve different problems. Delegation fits a user-directed task and preserves that user’s permission context; an app-only or workload identity fits unattended automation. In both cases, use narrow scopes, action-level checks, logging, and revocation.
How long should an agent’s credentials last?
Use the shortest practical lifetime tied to the task. Prefer short-lived credentials over long-lived ones, and remove elevated rights when the defined work or approval period ends. The exact lifetime depends on the workflow and its ability to renew access safely.
What if the task genuinely needs write access?
Grant only the specific write operation and resources required. Keep unrelated write, delete, messaging, spending, deployment, and permission-changing capabilities disabled, and require fresh approval for consequential effects.
What is the minimum information an audit log should contain?
Record the agent identity, effective permissions or scope, operation, target resource, authorization and approval outcome, result, and the initiating user or responsible organization where applicable.
How do I know whether revocation works?
Exercise the shutdown path. Disable the agent, rotate or revoke credentials, invalidate active tokens, and remove stale permissions, then verify that subsequent and already-running calls cannot continue outside the intended policy.
The Bottom Line
The safer alternative to broad account access is a layered design: delegated authority for user-directed work or a named workload identity for unattended work; task-specific, short-lived permissions; backend checks for every tool call; read-only defaults; isolation; specific approval for high-impact actions; complete logs; and a tested revocation path. These controls reduce blast radius and improve accountability without pretending that an AI agent is automatically trustworthy.
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