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An AI agent can use only the files, apps, tools, credentials and computing environment made available to it—but access can add up across connected services. To judge what an agent can really do, check four separate controls: its identity and granted data scopes, the actions its tools allow, where it runs, and which actions require approval. An approval prompt is not the same as revoking access.
What an AI agent’s permissions actually control
“Permission” is not one universal switch. An agent’s effective access comes from the combination of its identity, connected accounts, tools, credentials and execution environment. A narrow grant in one place does not necessarily narrow access elsewhere.
- Identity: Is the agent acting as a signed-in person, or under an identity created for the agent?
- Data scope: Which files, folders, app resources or organizational data can it reach?
- Action scope: Can it read only, or also edit, send, delete, export or change permissions?
- Execution environment: Does it run on a local computer, in a hosted sandbox, or in both?
- Approval and oversight: Which actions require a person’s approval, and what activity is logged?
These are distinct layers. A sandbox can constrain code execution without changing what a connected app authorized. An approval gate can require confirmation without narrowing the underlying account access.
What can an agent access in your files?
File access depends on what the agent’s environment exposes: perhaps selected files, particular folders, mounted storage or a broader filesystem. It also matters whether access is read-only or permits changes. Do not assume that describing a boundary in chat enforces it; the host application or operating system must impose the boundary.
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OpenAI’s sandbox guidance says generated code can access the files, credentials and network available in its environment. It recommends isolated compute, controlled network egress and careful credential handling. Its local-work guidance treats filesystem permissions and sandboxing as local execution controls, separate from global policy settings.
For a particular setup, identify which locations are actually exposed and what operations are permitted. If the agent can write, consider whether it could overwrite or delete important material; if it can reach credentials, consider what systems those credentials unlock. The agent’s visible file list alone may not reveal every resource available to code running in its environment.
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What an app connection or permission prompt means
A connected app involves at least two layers: the authorization granted by the external service, and the AI workspace’s controls over which app actions are available and when the agent must ask first.
OpenAI explains that ChatGPT app permission settings determine when it asks before reading or acting; those settings do not grant the app new access. Available data and actions depend on the app, the access granted when it was connected, and workspace controls. To remove access, disconnect the app or ask an administrator to disable it. See Connected apps in ChatGPT and admin controls for apps.
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Action constraints are not necessarily data filters
For Workspace Agents, connector action constraints can narrow what the agent may ask an app to do. OpenAI says those constraints do not filter the data returned through an otherwise allowed connector action. They limit actions, but should not be treated as a general data-loss-prevention filter. The distinction matters: an agent restricted from one kind of change may still receive data through another permitted action. See Workspace Agents for Enterprise and Business.
Check whose credentials a published agent uses
OpenAI warns that publishing an agent with its builder’s personal connection can let other users act through the builder’s credentials. Restrict the audience, grant only the access needed for the agent’s purpose, and audit use. Workspace-specific availability and controls can vary; consult the documentation for the applicable account and settings at Workspace Agents.
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What “computer access” can mean
Computer access may refer to tools and files on a connected local machine, or to resources in a cloud sandbox. These are separate environments: do not assume a setting in one automatically applies to the other. OpenAI’s local-work guidance describes local filesystem permissions and sandboxing as environment controls and says cloud and local settings do not automatically transfer across execution environments.
Network access belongs in this boundary too. Code that cannot see a particular file might still send available data to an external service if network egress is permitted. OpenAI’s sandbox security guidance highlights the files, credentials and network available to sandboxed code, and the need to control egress. Check separately what the environment can read, where it can write, which credentials it can use and which destinations it can contact.
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Identity and permissions: user-delegated versus agent-owned
The identity model affects what an agent can do and whose authority it uses. Microsoft distinguishes delegated permissions, in which an interactive agent acts on behalf of a signed-in user, from application permissions, which let an autonomous agent run without a user. Microsoft also describes resource-level role-based access control (RBAC), access packages and per-team Teams consent as ways to scope access. These are Microsoft-specific implementation options, not universal requirements for every platform. See Microsoft’s guidance on granting agents access to Microsoft 365 resources.
Microsoft recommends: “Use a unique, dedicated agent identity with a named owner/sponsor and approver.” Its least-privilege guidance also calls for documenting an agent’s purpose, approved data, dependencies and operating environment; reviewing effective permissions across roles, tools and downstream systems; denying unreviewed tools and integrations by default; logging identity, scope, action, resource and correlation ID; and testing revocation.
How to limit an AI agent’s access
- Define the task and approved resources. State what the agent is for, what data it may use and what systems it depends on. Give it access to the specific files or resources needed, rather than a broad account or tenant grant when narrower access is available.
- Use a dedicated identity where possible. Assign an owner or sponsor and an approver. For a Microsoft deployment, determine whether delegated or application permissions are appropriate, then use available resource-level controls to scope them.
- Enable only necessary tools and actions. Review what each integration can do, not just its name. Deny unreviewed tools by default, and distinguish an action restriction from a restriction on data the connector can return.
- Constrain the execution environment. Limit exposed files, credentials and network destinations. Check local and cloud environments separately, including where code runs and whether it can send data out.
- Put human review around high-impact actions. Require approval for sensitive or irreversible operations such as sending consequential messages, deleting data, exporting records or changing permissions. Treat approval as an additional safeguard, not a substitute for restricting identity permissions.
- Log and review activity. Record the agent identity, scope, action and affected resource so activity can be investigated. Microsoft’s shared-responsibility guidance also recommends auditing tool calls and checking authorization for every action.
- Test the off switch. Confirm that disabling the agent and removing or invalidating its credentials, tokens and stale grants actually prevents further access. Recheck downstream systems rather than assuming that turning off the agent alone revokes every connected authorization.
Microsoft’s AI agent shared responsibility model recommends least privilege for each tool, authorization checks for every action, human review for high-impact or irreversible actions, auditing tool calls, sandboxing and egress control for code execution and browsing, and isolation and access control for memory. It also warns that untrusted content—such as retrieved documents or tool outputs—can try to steer an agent into tool actions. No single safeguard replaces the others.
How to compare two agent setups
Compare the actual configuration, not a vendor-wide label or a single permission prompt. Product defaults and availability can differ by plan, workspace and environment, and may change. Check the current documentation for the specific setup.
| Check | What to establish |
|---|---|
| Identity model | Does the agent act on behalf of a signed-in user, or run under an agent-owned identity? |
| Data scope | Are access grants limited to specified files or resources, or do they reach a broad account or tenant? |
| Action scope | Can the agent read only, or also write, send, delete, export or change privileges? |
| Execution location | Does it run on a local computer, in a hosted sandbox or in both environments? |
| Network and credentials | Which credentials are available to it, and where can its environment send data? |
| Approval gates | Which high-impact actions require a person to review them? |
| Audit and ownership | Who owns the configuration, what activity is recorded, and how quickly can access be revoked? |
These checks describe the boundaries to verify; they do not establish that one vendor or product is safer than another without reviewing the exact configuration.
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