Give an AI agent only the tools, data, credentials, network access, and execution authority its task requires. Disable tools it does not need, narrow the actions available through retained tools, isolate the agent’s runtime, and require human approval before consequential actions that must not happen automatically. Then inspect logs to confirm the controls are working.
What does least privilege mean for an AI agent?
Least privilege is not one permission switch. It is a set of boundaries around what an agent can reach and do: which tools are available, which actions those tools may perform, what data and credentials the agent can access, where its code runs, and which network destinations it can contact.
These controls are not interchangeable. A policy that pauses a tool call does not remove that tool; a connector restriction on requested actions may not limit the data returned; and a network allowlist does not make an overpowered credential safe. Build the controls in layers, with each one addressing a distinct route to unwanted access.
How do you limit an agent’s access?
Use this sequence when designing or reviewing an agent. The inventory and deployment workflow below is a practical method derived from the documented control distinctions; it is not a standard procedure prescribed by any one vendor.
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Define the task and map its authority
Write down what the agent must accomplish, which data sources and tools it needs, and which actions it may take. Separate reading from sending, editing, posting, or deleting. Record the agent identity, credential owner, application owner, and execution environment. This helps expose unnecessary authority before implementation.
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Remove tools and narrow retained actions
Disable tools the task does not require. For tools that remain, restrict access to relevant apps, documents, action types, recipients, or destinations wherever the platform supports it. For example, OpenAI Workspace Agents documents connector action constraints such as limiting an email action to a recipient domain or allowing reads from a particular document. Those constraints govern what the agent can ask the connector to do; OpenAI cautions that they do not filter data returned by an otherwise permitted action. See OpenAI’s Workspace Agents documentation.
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Give the agent a narrowly scoped identity and credentials
Use a dedicated service identity when a shared agent-owned account is necessary, and grant it only the permissions required for the workflow. Prefer short-lived credentials where supported. Google’s guidance says, “Use least-privilege service accounts or API keys,” and also recommends short-lived tokens. A credential made available inside the agent’s environment should be treated as accessible to its code: OpenAI warns that agent-generated code can read environment keys. Keep an application API key outside that environment; for third-party credentials, use a managed secret reference or a trusted proxy to provide access only to approved destinations. Sources: Google Gemini API agents guidance and OpenAI sandbox security guidance.
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Isolate execution and restrict network egress
Run agent workloads in isolated compute, and separate environments when users or workloads must not share data. Allow outbound connections only to destinations the task needs—or disable network access if it is unnecessary. Configure the rule for the actual connection path: OpenAI distinguishes executor MCP connections from remote MCP connections. Google’s managed agent environment allows unrestricted outbound traffic by default; its documentation describes allowlists for restricting destinations or disabling outbound access. See OpenAI’s runtime security guidance and Google’s managed agents overview.
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Put consequential actions behind the right approval
Decide which actions may run automatically and which need a person to review them first. Approval should be attached to the relevant tool or action, not treated as a substitute for limiting the toolset and credentials.
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Inspect activity and verify outputs before deployment
Use available logs to check permission decisions, tool calls, results, and network outcomes. Also verify generated code, data transformations, and configuration changes before deploying them, especially when they modify data or interact with external systems. Output verification is a separate safeguard, not an access-control boundary.
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When should a tool call require human approval?
Require a person to review an action before execution when an error or misuse could send, edit, post, or delete content, or otherwise produce an outcome that should not be automated. The exact policy controls vary by platform.
Anthropic Managed Agents
Anthropic documents three server-side permission policies: always_allow runs without confirmation, always_ask pauses for approval, and auto evaluates each call and may allow, deny, or pause it. Toolset and individual-tool policies are available, but defaults differ between the agent toolset and MCP toolsets. Most importantly, auto is not a mandatory human checkpoint: a call judged safe may run before anyone sees it. Use always_ask for the relevant tool when review must happen before execution. These policies apply to server-executed agent and MCP tools, not custom tools executed by the application. See Anthropic’s permission policy documentation.
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OpenAI says connector write actions default to “Always ask” and documents optional custom approval settings for supported actions. Review those settings for any action that sends, edits, posts, or deletes content; do not assume every connector action has the same approval behavior. See OpenAI’s Workspace Agents documentation.
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How do controls differ across platforms?
The following controls are specific to the products and documentation cited. Check the settings and runtime that apply to your deployment rather than assuming the same control has identical scope across vendors.
| Platform documentation | Controls described | Scope or caveat |
|---|---|---|
| OpenAI Agents API sandbox security | Isolated compute, approved outbound endpoints, separated application keys, and vault-secret or proxy-based credential brokering. | Agent-generated code can access files, credentials, and network resources available to its environment. Keep the application API key outside it. |
| OpenAI Workspace Agents | App and connector selection, service-account guidance, write approvals, and connector action constraints. | Action constraints do not filter data returned by an otherwise permitted action. |
| OpenAI ChatGPT agent workspace controls | Role-based availability, app enablement, and website blocking by exact domain or domain plus subdomains. | The documented controls are for Enterprise and Edu. Website blocking is requested through an account team or support. |
| Anthropic Managed Agents | always_allow, always_ask, and auto policies at toolset or individual-tool level; events can include evaluated permission outcomes. |
Defaults differ by toolset. Policies do not cover application-executed custom tools, and auto does not guarantee human review before a call runs. |
| Google Gemini API managed agents | OS-level sandboxing, network allowlists, managed credentials, least-privilege identities, short-lived tokens, and human oversight. | The documentation, last updated 2026-09-17 UTC, says managed agents are in Public Preview and advises review before relying on them for sensitive workflows. Outbound network access is unrestricted by default. |
How can you tell whether the controls are working?
Review the records available in the specific runtime, then compare them with the policy you intended to enforce. OpenAI describes Codex telemetry covering prompts, tool approval decisions, execution results, MCP server usage, and network proxy allow-or-deny events. Anthropic managed-agent events can carry an evaluated permission outcome and, for auto, a reason code. These records support investigation and policy tuning; logging provides visibility, while enforcement still depends on runtime boundaries, permission checks, credentials, and network controls. See OpenAI’s Codex safety overview and Anthropic’s permission policy documentation.
Quick Recap
- Confirm that removed tools are unavailable, not merely awaiting approval.
- Test whether retained tools can reach only their intended actions and data.
- Verify that credentials are scoped correctly and are not exposed to code that does not need them.
- Check allowed and denied network destinations from the agent’s actual execution path.
- Confirm that actions requiring prior review pause before execution.
- Inspect logs for unexpected calls, permission outcomes, execution results, and network decisions.
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