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You can let an AI assistant do useful work without handing it unrestricted access—but a careful prompt is not a security boundary. Control comes from limiting what the assistant can reach, separating read access from write actions, requiring approval for consequential steps, and enforcing those rules where actions execute.
The exact controls differ across products and accounts. The sequence below gives you a practical setup for a connected assistant, with separate notes for ChatGPT workspaces and custom agent workflows.
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1. Define the task and its boundaries
Before connecting tools, write down the job in terms you can check: which accounts and data the assistant needs, what it may do, and what is outside its scope. “Prepare a reply for review” is narrower than “manage my inbox”; “update this record” is narrower than “maintain the customer database.”
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2. Grant only the access the task needs
Inventory every connected app or tool, then enable only the necessary capabilities. Where separate controls are available, treat reading and changing information as different permissions. A task that summarizes documents may need read access, not permission to edit or share them.
How ChatGPT app permissions fit together
ChatGPT connected-app controls vary by account, app, connected account, and workspace. Depending on what is supported, permission choices may include asking before reading or changing information, allowing reads while asking before changes, allowing some low-risk actions with additional review for higher-risk actions, or allowing supported actions without extra prompts. The last option carries greater risk and is not available in the standard account-wide or workspace-wide selectors. See Managing app permissions in ChatGPT for current details.
These controls are not interchangeable: an app permission does not connect an account, grant provider authorization, change access rights in the source service, or override workspace policy. Changing the app permission also does not disconnect it or revoke provider access already granted. To stop future access, you may need to disconnect the app or unlink authorization with the provider.
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How ChatGPT workspace controls fit together
In managed workspaces, role access determines who can use an app; enabled actions determine what it can do; and permissions govern when ChatGPT asks before using an available action. Provider approval and OAuth scopes are separate checks. Also, “Disable new actions” applies to actions introduced later—it does not turn off actions that are already enabled. Administrators may set app controls, and some sensitive actions may be denied rather than presented as an approval prompt. Details are in OpenAI’s admin controls for plugins and apps.
3. Match approval to the consequence
Set approval requirements according to what an action can affect, not just how often it occurs. Allow routine steps that are easy to inspect and reverse only when the product gives you an appropriate control. Require a person to review consequential actions where possible, such as sending a message, inviting someone, editing or deleting a record, changing access or security settings, making a purchase, or sharing sensitive personal, financial, health, or identity data. These are examples from OpenAI’s app-permission guidance, not a universal risk classification.
For a lengthy task, review the proposed plan before it runs, then keep separate approval gates for sensitive steps. Anthropic describes Plan Mode in Claude Code as a way to review and edit a plan before execution. It also cautions that excessive approval prompts can become friction and be tuned out. These are product-specific examples, not features guaranteed across assistants; see Anthropic’s discussion of trustworthy agents.
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4. Enforce rules where the action happens
If you build a custom agent, put a policy check next to every tool that can cause a side effect. Check the proposed target, action, arguments, calling identity, and scope. Deny calls that exceed the task boundary; pause ambiguous or high-risk calls for explicit human approval before the tool executes.
Do not assume a general instruction or a check around the agent’s input and output will inspect every tool call. OpenAI’s API documentation states: “If you need checks around every custom tool call in a manager-style workflow, don’t rely only on agent-level input or output guardrails.” Its guardrails and human review guide also describes an interruption-and-resumption approval lifecycle: preserve a pending run when review is delayed, then resume that same run after the decision rather than starting a fresh sequence.
Applications using the OpenAI API or Agents SDK do not automatically inherit Codex Auto-review. The application must implement its own review and enforcement, particularly for tools that change external state.
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5. Put independent limits around the execution environment
Approval rules and technical restrictions solve different problems. Approval determines when work must stop for a person; a sandbox limits where the agent can operate, such as which files it may write or whether it can access the network. Use both where appropriate rather than treating one as a substitute for the other. OpenAI describes this layered approach in Running Codex safely at OpenAI.
For a sensitive custom workflow, set independent boundaries for filesystem paths, network access, identity, and project scope. If a reviewer or policy service is unavailable or times out, fail closed for actions that require review: do not execute them by default.
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Web pages, documents, emails, and messages can contain prompt injection: untrusted text designed to override instructions, redirect tool calls, or expose information. Do not place retrieved text in developer instructions as if it were trusted. Limit what each tool receives to the data it needs, and use structured outputs to constrain information passed between workflow steps. OpenAI explains these risks in Safety in building agents.
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Guardrails do not eliminate the risk that a model may send more information to a connected MCP server than a user expected. Anthropic likewise recommends layered defenses and careful choices about tools, data, permissions, and operating environments in its trustworthy-agents guidance.
7. Keep an audit trail and revisit access
Record enough to reconstruct what the assistant attempted and what happened: the request, relevant tool call and arguments, approval or denial, execution result, and applicable policy or network decision. OpenAI reports that Codex supports OpenTelemetry export for events including prompts, tool approval decisions, tool results, MCP usage, and network allow-or-deny events in Running Codex safely at OpenAI.
Review permissions when the task changes, an account is replaced, or a tool gains new actions. Remove capabilities that are no longer needed, and separately revoke provider authorization or disconnect the app when you need to stop its access.
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What to check before enabling an assistant to act
- Scope: Is the task, target, account, and time or project boundary specific?
- Access: Does the assistant have only the tools and read/write actions needed?
- Approval: Which actions need human review, and what happens if review is unavailable?
- Enforcement: Are checks applied at every consequential tool call, outside the assistant’s instructions?
- Environment: Are filesystem, network, identity, and project access limited independently?
- Untrusted input: Are retrieved pages and documents treated as data, with only necessary information passed to tools?
- Audit: Can you determine what was attempted, approved, denied, and executed?
Standards are still developing
NIST announced its AI Agent Standards Initiative on February 17, 2026, covering work on industry-led standards, open protocols, and research into agent security and identity. The announcement described an ongoing initiative, with further research, guidelines, and deliverables to follow—not a completed standard. See NIST’s announcement.
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