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What “permission” means for an AI agent
People use the word permission to describe several different controls. Keeping them separate prevents a dangerous assumption: a successful API call proves technical access, not that the access was authorized.
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- Company authorization: the organization has approved the agent, its owner, its purpose, and the systems it may use.
- User authority: when the agent acts for an employee, the employee is allowed to access the relevant data and perform the requested task.
- Technical authorization: identity and access controls accept the agent, user, tool call, target resource, and requested action.
- Legal or contractual authority: the company is permitted to process the particular data under applicable law and agreements.
Security guidance can establish the first three controls, but it cannot determine the legal answer for every country, industry, contract, employee record, customer record, or regulated dataset. A privacy officer or qualified lawyer must assess that fact-specific question.
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An agent may be able to read a shared drive, call an internal API, or invoke a plugin because someone configured a broad credential. That capability does not establish that the organization approved the use, that the acting user is entitled to the data, or that the requested operation is within the agent’s purpose.
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Microsoft’s official identity guidance describes the required baseline this way: “Identity controls ensure that every user, agent, workload, plugin, and tool is explicitly identified and authorized before it can access data or perform an action.” The same guidance says to “bind permissions to the initiating principal and task, limit them to the minimum required scope, and issue them for the shortest practical duration.”
In practical terms, an authorization decision should answer all of these questions before the call is allowed:
- Which named agent, workload, or user is making the request?
- Who owns and approves that identity?
- What task is being performed?
- Which exact data source, record, file, or API target is involved?
- Which operation is requested: read, create, modify, send, delete, purchase, deploy, or change permissions?
- How long should the authorization remain valid?
Whose permission should an agent use?
The answer depends on the access pattern. Two patterns are common, and each requires explicit controls rather than an inherited “allow everything” credential.
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In a delegated model, the agent acts on behalf of a particular employee or other initiating user. Microsoft Azure guidance says such an agent should inherit that user’s permissions and that the user’s identity or token should be passed securely.
The agent must not become a back door around the user’s restrictions. If the employee cannot open a confidential folder, the agent should not be able to open it for the employee. Authorization should also remain attributable: logs need to show both the agent and the user whose authority was delegated.
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Dedicated agent identity
A dedicated identity is appropriate when an agent runs a recurring workflow that should not depend on one employee’s personal account. The organization assigns it a named owner or sponsor, narrowly scoped roles, approved data sources, approved tools, short-lived credentials, and a documented shutdown process.
This model does not mean the agent receives broad administrator rights. Its permissions should be designed around the workflow, with separate identities or roles when different tasks require different data or actions.
| Decision area | Delegated/user-context access | Dedicated agent identity |
|---|---|---|
| Permission basis | The initiating user’s approved access and task | Roles and scopes approved specifically for the agent workflow |
| Attribution | Record the agent and the “on behalf of” user | Record the agent identity, owner, and task context |
| Credential handling | Pass the user identity or token securely | Use scoped, short-lived credentials; avoid permanent broad secrets |
| Best control question | Can the agent do only what this user can do? | Are the agent’s roles limited to the approved data and tools? |
| Revocation focus | Invalidate delegated tokens and remove user access | Disable the identity, rotate credentials, and remove its roles |
Neither pattern is automatically safer. Compare them by effective permission scope, the ability to constrain each tool action, audit completeness, approval for high-impact operations, and revocation speed.
What permissions should an AI agent receive?
Start with the smallest set of data sources and tools that can complete the stated task. Microsoft’s governance guidance puts it plainly: “Grant agents access only to the specific data sources required for their function.” A summarization agent may need read access to a defined document collection; it does not automatically need write access, mailbox access, deployment rights, or permission-management rights.
- Separate read and write roles. Give write access only when the workflow genuinely requires it.
- Constrain targets. Authorize specific repositories, projects, folders, records, environments, or API resources rather than an entire tenant.
- Constrain actions. A tool may be allowed to create a draft but not send it, or to propose a deployment but not execute it.
- Use short-lived scopes. Issue credentials for the shortest practical duration and renew them only while the approved task remains active.
- Review downstream permissions. An agent’s effective access includes permissions inherited through connectors, plugins, service accounts, groups, and linked applications.
- Deny unreviewed integrations. Plugins, tools, cross-tenant connections, and guest access should be blocked by default until an owner approves them.
How to keep confidential data away from public-facing agents
Public-facing agents should be separated from internal business data. Microsoft guidance recommends keeping confidential and public data in distinct boundaries and says a public-facing agent must not access internal business data.
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That separation should exist in the identity and network design, not merely in a prompt telling the model not to reveal secrets. Use separate data stores, credentials, connectors, environments, and monitoring policies where appropriate. Treat retrieval indexes, cached documents, conversation history, uploaded files, and tool outputs as potential copies of the underlying data. If a public agent does not need a source, do not connect that source to it.
Prompt instructions remain useful for behavior, but they are not an authorization boundary. A malicious prompt, accidental disclosure, or model error can defeat a textual instruction when the underlying credential still permits access.
A practical approval and implementation process
Use the following sequence before putting an agent into production.
- Write the purpose. Describe the business task, operating environment, expected users, data categories, and actions the agent will perform.
- Assign ownership. Name a business sponsor, technical owner, security approver, and replacement contact. Record who can request changes or emergency shutdown.
- Choose the principal. Decide whether the workflow needs delegated user context or a dedicated agent identity. Document why.
- Map the data flow. List every source, index, connector, plugin, API, storage location, and downstream system the agent can reach.
- Define effective permissions. Check inherited roles, group membership, service-account rights, guest access, and cross-tenant paths, not just the visible role assigned to the agent.
- Authorize each tool. Bind every call to an identity, specific action, target resource, and task. Deny tools that have not passed review.
- Set credential limits. Use narrowly scoped, short-lived credentials. Store secrets in an approved secret-management system and rotate them on a schedule and after incidents.
- Add human gates. Require fresh confirmation before irreversible or high-impact actions such as sending messages, deleting data, making purchases, deploying changes, or changing permissions.
- Turn on audit records. Capture the agent identity, role, effective scope, action, resource, timestamp, correlation information, and acting user when applicable.
- Test failure and shutdown. Verify that disabling the agent, invalidating tokens, rotating credentials, and removing roles actually stop access.
- Approve the release. Keep the purpose, data inventory, permissions, test evidence, and approval decision together so reviewers can reconstruct why access was granted.
What should be logged and reviewed?
Logs should allow an investigator to reconstruct not only what happened, but under whose authority and with which effective rights. At minimum, record:
- the unique agent or workload identity;
- the owner, role, and effective permission scope;
- the initiating user when the agent acts on someone’s behalf;
- the tool, action, target resource, and result;
- timestamps, request or correlation identifiers, and relevant policy decisions;
- credential issuance, renewal, rotation, and revocation events.
Review those records for unexpected data sources, unusual targets, privilege expansion, failed authorization attempts, and actions outside the declared workflow. Re-review access when the agent’s workflow, model, tools, data scope, owner, or deployment environment changes. A permission review that covers only the initial configuration will miss later connector and role changes.
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How to revoke access when an agent no longer needs it
Revocation is a tested operation, not a promise to “remove it later.” Maintain a documented runbook that can be executed by someone other than the original developer.
- Disable the agent or workload identity.
- Invalidate active sessions, delegated tokens, refresh tokens, and cached credentials.
- Rotate or revoke secrets used by connectors and plugins.
- Remove direct roles, group memberships, API grants, guest links, and downstream permissions.
- Disable scheduled jobs, webhooks, queues, and automations that can recreate access.
- Preserve relevant logs and identify data the agent may have copied into indexes, caches, files, or conversation histories.
- Confirm with a test request that the old identity cannot read or change the protected resource.
Test this procedure before production and after material architecture changes. If an agent’s owner leaves the company or its purpose ends, the same process should be available immediately, without waiting for a normal review cycle.
When must a person approve an action?
Require fresh human confirmation for operations that are irreversible, costly, externally visible, or capable of changing future access. Microsoft guidance specifically identifies sending, deleting, purchasing, deploying, and changing permissions as examples.
The approval should identify the exact action and target, show the relevant inputs, state who will be accountable, and expire if not used promptly. “Approve all future actions” is not equivalent to confirmation of a specific high-impact operation. For lower-risk read-only work, automated authorization may be appropriate when the scope, identity, and monitoring controls are already established.
Common failure modes and their fixes
A shared administrator credential is used for convenience
Risk: The organization cannot attribute actions or limit the agent to one workflow. Fix: Create a dedicated identity or secure delegated flow with narrow roles and short-lived credentials.
The agent can reach a confidential repository through a plugin
Risk: The visible agent role looks narrow while the plugin supplies broader downstream access. Fix: Inventory effective permissions across every connector and authorize each tool-target pair separately.
A prompt says “never disclose internal data”
Risk: Instructions are treated as a substitute for an access boundary. Fix: Remove the confidential source from the public agent’s identity, network, connector, and retrieval index.
A user loses access but delegated tokens continue working
Risk: The agent retains stale authority after the user’s role changes. Fix: Invalidate active tokens, re-evaluate the user’s current permissions on every sensitive operation, and test revocation.
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Risk: Investigators cannot determine which person initiated an action or which effective scope was used. Fix: Record the agent, acting user, task, target, action, role, and correlation information together.
What current guidance says—and does not say
Microsoft’s governance framework describes a baseline covering agent ownership, identity and lifecycle, data governance and compliance, security, and development standards. Its identity guidance was reported as last updated August 1, 2026.
NIST’s National Cybersecurity Center of Excellence announced a concept paper on February 5, 2026, exploring how identity standards and practices might apply to software agents. The announcement discussed risks from agent access to diverse datasets, tools, and applications, including identification, authorization, auditing, non-repudiation, and prompt-injection controls. It described a potential project and requested feedback; it should not be treated as a finalized NIST agent-access standard.
These materials support a security-governance approach. They do not decide whether a specific company has legal authority to process employee, customer, confidential, or regulated data. That determination requires the applicable jurisdiction, contracts, internal policies, data classification, and deployment details.
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Quick Recap
A concise governance checklist
- Every agent has a unique identity, named owner, sponsor, and approver.
- The purpose, data sources, tools, environment, and permitted actions are documented.
- Effective permissions are reviewed across roles, connectors, plugins, and downstream systems.
- Public-facing agents are isolated from confidential internal data.
- Each tool call is authorized for a specific identity, action, target, and task.
- Credentials are scoped, short-lived, protected, and rotated.
- High-impact actions require fresh human confirmation.
- Logs include identity, effective scope, action, resource, acting user, and correlation data.
- Disable, token-invalidation, credential-rotation, and permission-removal procedures are tested.
- Access is re-approved after material workflow, owner, tool, data, or environment changes.
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