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An AI agent can see, save, and act only within the intersection of three boundaries: the tools and data sources you expose, the identity or OAuth token it uses, and the policy checks enforced by the application or API. Signing in does not give an agent unlimited authority, but an apparently narrow permission can still expose sensitive data or enable consequential actions. Treat access, retention, and write authority as separate decisions, and require a fresh approval before anything difficult to undo.
What an AI agent permission actually means
An agent is software that can interpret a goal, call tools, and carry out a sequence of steps. A permission is not a general statement that the agent is “trusted”; it is an authorization to use particular resources through a particular identity under particular policies.
The effective permission is the intersection of:
- Exposed capabilities: the connectors, APIs, files, browser sessions, and application actions made available to the agent.
- Identity and token: the signed-in user, service account, workload identity, or OAuth token presented to each system.
- Enforcement: scopes, resource-level rules, approval gates, tenant policies, and API checks that accept or reject each request.
Microsoft Entra describes two common authorization forms. Delegated permissions let an agent act in the context of a signed-in user. Application permissions let an app use its own identity, potentially without a person present. Microsoft also blocks selected high-risk, tenant-wide permissions for agents. AWS similarly distinguishes explicit user delegation from autonomous operation and recommends verifiable authentication and least-privilege controls.
That model explains why a chat window is not a security boundary. The interface may display a friendly instruction, but the connected service ultimately decides whether a read or write request is allowed. Put authorization checks at the API or data layer rather than relying only on what the agent says it will do.
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What can an AI agent see?
An agent can observe whatever its enabled tools return. Depending on the product and your approval, that may include email, cloud files, records in a business application, webpages, screenshots, and account settings.
Email and messages
If an integration receives a mailbox-read scope such as Microsoft Graph Mail.Read, the agent can retrieve the messages that the token is allowed to read. The scope does not automatically grant the ability to send, delete, or change mailbox rules; those are separate capabilities and should be authorized separately. A broad mailbox scope can nevertheless reveal years of confidential correspondence, attachments, and personal information, so constrain the mailbox or folders where the platform permits it.
Cloud files
Microsoft’s bounded example is a user’s OneDrive data when Files.Read is granted. The agent can inspect files returned by that permission, but it should not be assumed to have access to every tenant document or to write those files. Read and write scopes are distinct. Sharing, moving, deleting, or changing permissions should be treated as higher-risk actions than opening a document.
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A computer-use agent observes the browser interface in its hosted session. What it can see includes rendered page content, forms, navigation state, and sometimes information revealed after you sign in. The session is still bounded by the account and site permissions available in that browser. A site may expose more information than an API scope would, so review which tabs, profiles, cookies, and accounts are present before handing over control.
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Settings and connected accounts
OpenAI’s ChatGPT agent documentation names emails, files, and account settings as examples of sensitive data it may access after sign-in. An agent that can open account settings may be able to read configuration details even when it cannot change them. Do not infer write authority from visibility: reading a setting and changing it are separate permissions or policy decisions.
What can an agent save or remember?
Authorization and retention answer different questions. A connector approval says what the agent may access; it does not, by itself, say how long conversations, task outputs, screenshots, tool traces, or service logs are retained.
OpenAI’s app-permission documentation explicitly separates app permissions from Memory, personalization, data-retention, and model-training controls. Check all of those settings independently, including controls in the agent product, your workspace or organization, and the connected service. A connector can be disconnected while copies of previous task content remain subject to the product’s retention policy.
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Three kinds of “remembering”
- Working context: information the agent holds while completing the current task, such as retrieved messages or a page it has opened.
- Conversation or task history: prompts, outputs, screenshots, and tool events retained so a session can be reviewed or resumed.
- Persistent memory or personalization: facts or preferences stored for later conversations, if the product supports and enables that feature.
Ask the provider which category applies to each data type, who can access retained records, and how deletion requests work. Do not treat a “memory off” switch as proof that short-term task logs or connected-service records are immediately deleted.
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What can an AI agent do on your behalf?
With browser or application tools, an agent may navigate sites, collect information, fill forms, use business software, share files, or modify account settings when the relevant capability and authorization are available. The practical dividing line is whether an operation only reads information or changes an external state.
Lower-risk reads
- Search a permitted mailbox or file store.
- Summarize documents or gather information from approved websites.
- Inspect an account setting without changing it.
Write actions that deserve an approval gate
- Sending email, messages, invitations, or public posts.
- Editing or deleting files, records, calendar events, or tickets.
- Purchasing goods, approving payments, or submitting forms.
- Changing security, billing, sharing, forwarding, or recovery settings.
- Sharing a file or granting another person access.
OpenAI states that its computer-use agent cannot grant permission or override the user’s instructions. That limitation does not make every permitted action safe: an agent can still make a damaging change if the user or policy has already authorized it. Configure confirmation for consequential writes, and make the confirmation describe the exact target, values, recipients, and irreversible effects.
Delegated access versus autonomous access
| Model | Identity used | Typical operating condition | Main control question |
|---|---|---|---|
| Delegated user access | A signed-in person’s token and approved scopes | The agent acts for that user, often during an interactive session | Exactly which resources and actions did the person consent to? |
| Application or workload access | An agent, app, service account, or workload identity | It can run without a person present | Who owns the identity, and how is its scope prevented from expanding? |
| Hybrid workflow | Agent identity plus a human approval step | Automation prepares work; a person authorizes selected writes | Which actions are blocked until fresh approval? |
Delegated access does not mean the agent becomes a human, and autonomous access does not mean it is anonymous. The identity determines what the downstream service can authorize. Microsoft records approved OAuth grants and distinguishes bounded user data from tenant-wide administrative power. AWS recommends separating agent and human permissions and preventing privilege creep.
Keep agent identities separate from human administrator identities. An administrator’s credentials can turn a routine automation mistake into a tenant-wide incident, while a dedicated workload identity makes scope, ownership, and revocation auditable.
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How to choose and configure safe permissions
- Write the task in resource-and-action terms. Specify the mailbox, folder, site, repository, record type, and exact actions required. “Manage email” is too broad; “read invoices in this folder and draft replies” is testable.
- Grant the smallest scopes. Prefer read-only scopes when the task does not require writes. If the platform supports resource, record, or action-level restrictions, use them instead of account-wide access.
- Separate preparation from execution. Let the agent draft a message, purchase order, or setting change, then require a human confirmation immediately before submission.
- Enforce at the API or data layer. Validate the caller, resource, action, and policy on every request. A prompt instruction such as “never delete” is not a substitute for an API rule that rejects deletion.
- Use distinct identities. Do not reuse a human administrator account for unattended work. Record the owning team and the business purpose for each agent identity.
- Log every tool call. Capture the user, agent identity, resource, time, requested action, approval context, result, and error. Logs should let an investigator reconstruct what the agent saw and changed.
- Review outputs for disclosure. A valid token proves authorization, not safe handling. Check whether the response combines confidential information from one user with an audience that should not receive it.
- Set an end date. Use expiry where available, and plan a review when a project, employee, or integration changes.
How to revoke an agent’s access
Revocation should stop future access, not merely hide the connector from the agent’s interface. Use the connected product’s account or app-management controls to disconnect the integration; OpenAI documents disconnecting an app to stop future access. Also revoke or disable the underlying OAuth grant, service credential, or workload identity in the identity platform when that option is available.
- Stop active agent runs and disable scheduled jobs.
- Disconnect the app or connector in the agent product.
- Revoke the corresponding OAuth consent, token, API key, or service credential in the connected identity system.
- Remove shared folders, delegated mailbox rights, browser profiles, and other resource-level grants that are no longer needed.
- Confirm through logs or a test request that a new read and a new write are rejected.
- Review retained conversations, screenshots, outputs, and service logs under the applicable retention policy.
Test revocation and expiry before production use. A control that has never been exercised can fail because a second credential, cached session, or separate scheduled job still exists.
Where permission boundaries can fail
A token is valid but the output leaks information
The OpenID Foundation warns that an agent acting with one user’s permissions may gather context that other users cannot access and include confidential information in its output. This can happen when a private message is summarized into a shared channel, when records from two users are combined, or when a document is sent to a recipient who was not covered by the original consent.
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The agent has more authority than the task needs
Broad scopes are convenient at setup but difficult to reason about later. A connector that can read an entire account may expose unrelated personal or regulated data, while a write-capable identity can make changes outside the original workflow. Narrow the scope, split read and write credentials, and review grants regularly.
The chat instruction conflicts with policy
A user may ask the agent to ignore a restriction, but the API or tenant policy should still reject the call. Conversely, a permissive policy can allow an action that the user did not realize was included in the consent screen. Make policy enforcement independent of natural-language instructions and show the exact scope before connection.
Questions to ask when comparing agent products
| Comparison axis | What to verify |
|---|---|
| Identity model | Does it support user delegation, a service identity, or both? Can each be separated and audited? |
| Scope granularity | Can access be limited by account, resource, record, folder, field, or action? |
| Write controls | Are writes automatic, approval-gated, or blocked? Can approvals expire or be limited to one operation? |
| Memory and retention | Which settings govern conversation history, screenshots, task outputs, personalization, logs, and model training? |
| Audit detail | Do logs identify the human requester, agent identity, resource, time, action, approval, and outcome? |
| Revocation and expiry | Can administrators disconnect apps, revoke grants, rotate credentials, and test that access has ended? |
| Enforcement location | Are controls enforced by the API and data layer, or only described in the chat interface? |
Use this checklist during a pilot, not only during procurement. A product can advertise least privilege while offering only account-wide scopes, or provide an approval button without a durable audit record.
A practical risk model for everyday use
Classify an action by both what it can expose and what it can change. Reading a public webpage is low consequence. Reading a private mailbox is more sensitive even if no external state changes. Sending a message, deleting a record, changing a recovery address, purchasing something, or sharing a confidential file is high consequence because the effect reaches another person or is hard to reverse.
- Read-only, low sensitivity: allow with narrow scopes and ordinary logging.
- Read-only, high sensitivity: limit resources and recipients; review outputs for disclosure.
- Reversible write: prepare automatically but request confirmation with a preview.
- Irreversible or high-impact write: require fresh approval, stronger authentication where available, and a complete audit trail.
This model avoids the common mistake of treating every permission prompt as equivalent. The same agent may be acceptable for searching a project folder and unacceptable for changing an account recovery method.
Bottom line: permission is not the same as trust
An AI agent acts as the identity and tools you give it, within the policies that enforce those grants. It can see connected data, retain task material under separate product rules, and perform writes when those writes are authorized. Use narrow, preferably read-only scopes; separate agent identities from people; enforce authorization at the API or data layer; gate consequential actions; log every call; and verify that disconnecting or revoking access actually blocks future requests.
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