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Before an AI agent runs, define the identity it acts as, the resources it may access, the operations it may perform, and the environment it runs in. Keep each boundary as narrow as the task allows, and enforce authorization where an action is executed—not by relying on the model to refuse an unsafe request.
What does it mean to decide what an agent can reach?
An agent’s reach is the combination of its available tools, the permissions of the identities behind those tools, the data and systems those identities can access, and the runtime environment’s filesystem and network access. A useful design question is: should this agent be allowed to perform this action, on this resource, under this authority?
These boundaries are related but not interchangeable. Removing a tool limits what the agent can ask to do. Narrowing a connected identity limits what the downstream system will permit. Sandboxing limits what the runtime can touch. An independent authorization check determines whether a particular operation is allowed at the moment it is attempted.
Why can an apparently simple agent become overpowered?
OWASP’s LLM06:2025 Excessive Agency describes risk from excessive functionality, excessive permissions, and excessive autonomy. These can combine: an agent intended to summarize email may have a mail extension that can also send or delete messages; hostile content in a message could then prompt it to disclose information. The failure is not just a model-quality problem. The agent has more capability and authority than its task requires.
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- Excessive functionality: The agent has tools or operations that are not needed, such as arbitrary shell execution when a task-specific function would suffice.
- Excessive permissions: A connected identity can access more data or perform more operations than the task calls for.
- Excessive autonomy: The agent can carry out consequential actions without a separate approval or policy check.
How should you define an agent’s access before deployment?
Start with the work the agent must do, then derive the smallest set of tools, identities, resources, and runtime capabilities that can complete it. Treat each tool call as a request for a specific action—not as proof that the action is authorized.
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Inventory the task and its boundaries
List the agent’s purpose, the data classes it needs, the systems and resources it may touch, the tools available to it, and the human or service identities involved. Distinguish reading from writing, and routine operations from actions with significant or irreversible effects.
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Assign an accountable identity and owner
Give the agent or workflow a named identity with an accountable owner. Where practical, tie access to the initiating user and the current task rather than using a broadly privileged shared account. Map each task to specific scopes and review the combined permissions granted through connected tools.
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Reduce the tool surface
Remove tools the task does not need. Prefer narrow, task-specific functions over open-ended capabilities such as arbitrary command execution or generic URL fetching. For an email-summary workflow, a read-only operation is a tighter boundary than a plugin that can read, send, and delete mail.
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Limit downstream permissions
Grant the connected identity only the rights required for the task. A read-only task should not inherit database write or delete rights, or access to other users’ data simply because the underlying account has those privileges.
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Constrain the runtime environment
Use isolated compute where appropriate, restrict outbound network access to approved endpoints, and keep application or third-party secrets outside agent-generated code environments where feasible. OpenAI’s Sandbox security documentation describes these environment controls; they reduce environmental reach but do not authorize actions in connected services.
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Set policy at the action boundary
Before carrying out each request, the system that executes or serves it should validate the identity, operation, target, scope, and any required approval. Deny the action if authorization or policy checks fail. Do not rely on the model’s own interpretation of its instructions as the access-control mechanism.
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Gate consequential actions and record them
Require human approval for high-impact or irreversible operations. Make the approval specific to the actor, tool, target, parameters, time, and expiry; do not treat general consent as authorization for every future action. Record identity, effective scope, action, resource, and correlation information so the event can be reconstructed.
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Test revocation and review changes
Test credential rotation, token invalidation, agent disablement, and removal of stale permissions. Reassess access when the agent’s tools, data scope, workflow, or environment changes.
How do the controls fit together?
No single control answers every access question. Use the layer that governs the risk, and do not mistake a boundary in one layer for a substitute for another.
| Control layer | What it limits or verifies | Design choice |
|---|---|---|
| Tool availability | Which operations the agent can request | Expose only necessary, narrowly defined functions; avoid broad tools when a task-specific operation will work. |
| Identity and permissions | Which resources and operations connected systems permit | Use task-appropriate identities and minimum necessary scopes; review aggregate permissions across tools. |
| Runtime isolation | What the execution environment can access, including files and network destinations | Isolate compute, restrict outbound connections to approved endpoints, and separate secrets from generated-code environments where feasible. |
| Action authorization | Whether a particular call is permitted for its identity, operation, target, and scope | Validate each action in the downstream or execution layer and deny when checks fail. |
| Approval and audit | Whether a consequential action has specific approval and can later be traced | Bind approval to the exact action and retain correlated logs. |
| Revocation and review | Whether access can be withdrawn and remains appropriate as the system changes | Exercise revocation paths and revisit access after material changes. |
What should happen when a tool call is made?
The model may propose an action, but an independent policy or execution layer should decide whether it can proceed. OWASP’s AI Agent Security Cheat Sheet recommends controls such as exact-action validation, approval, audit trails, and failing closed when authorization checks cannot be completed.
- Identify the actor and the authority under which the request is being made.
- Check the requested operation and target against the agent’s permitted scope.
- Confirm that any required approval applies to these exact parameters and has not expired.
- Reject the call if the identity, scope, policy result, or required approval is missing or invalid.
- Log the decision and enough correlation information to connect it to the request and its outcome.
Authorization belongs at the system that actually performs or serves the operation. A tool description, prompt instruction, or model-generated explanation is not a replacement for that check.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallHow can you tell whether an access design is too broad?
Review the design against the task rather than asking only whether the agent can complete a demonstration. For every capability, ask what it enables, whose authority it uses, and whether a narrower boundary would still meet the task.
- Can the agent perform an operation that the stated task never needs?
- Does a read workflow have write, send, delete, or administrative rights?
- Can a tool reach unrelated users’ data, systems, files, or network destinations?
- Would a broad command or generic fetch tool allow actions beyond the named task?
- Can a high-impact action proceed without approval tied to its exact target and parameters?
- Can the team identify the effective identity and scope for an action in its logs?
- Can access be disabled or revoked promptly, including downstream tokens and stale grants?
Which guidance informs these controls?
OWASP’s LLM06:2025 Excessive Agency covers least functionality, least permissions, user-context access, approval, downstream authorization, monitoring, and rate limiting. OWASP’s AI Agent Security Cheat Sheet addresses action previews, exact-action validation, approvals, audit trails, and fail-closed behavior. OpenAI’s Sandbox security documentation covers isolation, outbound endpoint restrictions, and credential separation. Microsoft Learn’s Identity, Access, and Least Privilege guidance discusses agent and tool identity, short-lived scopes, approval, and auditing; that page was last updated August 1, 2026.
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