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An AI agent can access only the files, apps, tools, credentials, and execution environment made available to it—but those permissions can add up across connected systems. To limit risk, check both what the agent is authorized to access and what it is allowed to do, then require review for sensitive actions.
What an AI agent permission actually controls
“Permission” can refer to several different controls. An agent’s effective access depends on its identity, the resources and credentials assigned to that identity, its connected tools, and the environment where it runs. A prompt asking you to approve an action is only one layer; it does not necessarily narrow the underlying access already granted.
- Identity: Which user or agent account is making the request?
- Data scope: Which files, folders, records, or account resources can it reach?
- Action scope: Can it read, edit, send, delete, export, or change permissions?
- Execution environment: Does it run on your computer or in a hosted sandbox, and what network and credentials are available there?
- Approvals and oversight: Which actions require a person’s approval, and what is logged?
These controls complement one another. A sandbox does not remove authorization a connected app has received, and an approval gate does not replace narrow identity permissions.
File access: check the actual boundary
Find out which specific folders, selected files, or mounted data the agent can see, and whether it can only read them or also change them. A conversational instruction such as “don’t edit my files” is not, by itself, a filesystem boundary.
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For code running in a sandbox, the relevant scope is what that environment exposes: files, credentials, and network access. OpenAI’s sandbox security guidance recommends isolated compute, controlled network egress, and careful credential handling. For local execution, filesystem permissions and sandboxing are separate environment controls; OpenAI’s local work guidance explains that cloud and local settings do not automatically transfer between execution environments.
Network access belongs in the same review as visible files. An agent that cannot see a sensitive file directly may still be able to send data it can access elsewhere if its environment has unrestricted network egress.
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Connected apps: approval is not the same as access
A connected app involves at least two layers: what the external provider authorized when the app was connected, and what the AI workspace permits the agent to do or requires it to ask before doing. OpenAI explains that ChatGPT app permission settings determine when it asks before reading or acting; they do not grant the app new access. Available data and actions depend on the app, the access granted at connection time, and workspace controls. See Connected apps in ChatGPT.
That distinction matters: requiring approval before an action does not revoke an app’s provider authorization. To remove an app’s access, disconnect it or ask the workspace administrator to disable it. OpenAI’s admin controls guidance describes administrator controls for apps.
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Action restrictions are not necessarily data filters
For ChatGPT Workspace Agents, connector action constraints can limit what an agent may ask an app to do. They do not filter data returned by an otherwise allowed connector action, so they are action restrictions—not a general data-loss-prevention filter. OpenAI also warns that publishing an agent using its builder’s personal connection may allow other users to act through that builder’s credentials. Restrict the audience, use least-privilege connections, and audit access. See ChatGPT Workspace Agents for Enterprise and Business.
“Computer access” can mean local or cloud access
Check local computer access and hosted execution separately. An agent connected to a local machine may have access to files and tools exposed by that machine’s permissions. A cloud sandbox has its own exposed files, credentials, and network. Settings for one environment do not necessarily govern the other.
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For either environment, ask what data is mounted or reachable, which credentials are present, and where network connections are allowed. OpenAI’s sandbox security guidance covers sandbox exposure and network egress; its local work guidance describes local controls and the distinction between local and cloud settings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an identity and scope that fit the task
For routine work, prefer a dedicated agent identity with access limited to the specific resources and tools required. Microsoft Learn recommends: “Use a unique, dedicated agent identity with a named owner/sponsor and approver.” Its least-privilege guidance for AI agents also recommends documenting the agent’s purpose, approved data, dependencies, and operating environment; reviewing effective permissions across roles and downstream systems; denying unreviewed tools and integrations by default; and testing revocation.
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Microsoft’s guidance distinguishes two identity patterns: delegated permissions let an interactive agent act on behalf of a signed-in user, while application permissions let an autonomous agent operate without a user. Microsoft-specific options such as resource-level RBAC, access packages, and per-team Teams consent can help scope access; they are examples for Microsoft environments, not universal controls. See Grant agents access to Microsoft 365 resources.
Use approvals for high-impact actions, with auditing
Keep routine, low-risk work within narrow permissions, and add human review for actions that are sensitive or hard to reverse. Examples include sending external messages, deleting or exporting data, changing access, or executing code with broad network access. Check authorization when each action is performed rather than assuming an earlier approval covers every later action.
Microsoft’s AI agent shared responsibility model recommends least privilege per tool, authorization checks for every action, human-in-the-loop gates for high-impact or irreversible actions, and auditing tool calls. It also recommends sandboxing and egress control for code execution and browsing, and isolation and access control for memory. Retrieved documents and tool outputs should be treated as untrusted input: malicious content can try to steer an agent into taking tool actions.
A practical permission review
- Identify the agent and owner. Establish whether it acts as a signed-in user or its own identity, and name the person responsible for its configuration.
- List what it can reach. Check files and folders, connected apps, downstream systems, credentials, memory, and network access in each execution environment.
- Separate reading from acting. Confirm whether each tool can read, write, send, delete, export, or change privileges. Remove capabilities the task does not require.
- Set approval points. Require review for sensitive or irreversible actions, but do not treat approval prompts as a substitute for narrowing the agent’s underlying access.
- Verify visibility and removal. Log the identity, scope, action, resource, and correlation ID where available. Test that disabling the agent and removing or invalidating its credentials, tokens, and stale grants actually stops access.
When comparing two setups, compare identity model, data scope, action scope, local versus hosted execution, network and credential exposure, approval gates, audit visibility, revocation speed, and who owns the configuration. Defaults and available controls vary by product, plan, workspace, and environment, so check the current documentation for the specific setup rather than assuming a vendor-wide rule.
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