What is AI agent authorization?

By Identra · Updated October 4, 2026

AI agent authorization is deciding and enforcing which actions an AI agent may take, on which resources, under whose authority and under what conditions. It turns an assigned task into hard access limits, including when a person must approve and when access ends.

How is agent authorization different from authentication?

An AI agent identity says which agent is asking. Authentication proves it. Authorization decides whether this specific action is allowed. A valid token says nothing about whether the action serves the task.

Agents pick their own tools and targets mid-task. So a working connection to Salesforce can't mean permission to do everything the Salesforce API offers. The decision has to cover the actual record and the actual operation.

Authentication
Question it answers
Which identity is asking?
Example
Verify the support agent's credential
Authorization
Question it answers
May it do this?
Example
Allow reads on the assigned customer's ticket only
Human approval
Question it answers
Has a person signed off on this step?
Example
Approve one specific refund before it runs

Control	Question it answers	Example
Authentication	Which identity is asking?	Verify the support agent's credential
Authorization	May it do this?	Allow reads on the assigned customer's ticket only
Human approval	Has a person signed off on this step?	Approve one specific refund before it runs

Whose authority does an AI agent use?

Either the user's, through delegation, or its own as an application or workload identity. With AI agent delegation, effective access should stay inside what the user can do, what was delegated and what the task policy allows. A tenant admin who asks an agent for a read-only report shouldn't lend it admin rights.

An agent that runs on its own needs an owner, a purpose and permissions that fit the work. Logs should name the agent and, where there is one, the delegating user. When one agent hands work to another, the limits go with it. No quietly swapping in broader credentials.

Permission isn't intent. A user who can export the whole CRM may only have asked for a summary. The task limit is what makes that difference enforceable.

What does agent authorization look like in a real workflow?

Say a support agent is looking into a disputed invoice. It needs that customer's ticket, that invoice and a draft reply. It doesn't need other customers, bank detail changes or bulk export.

The application checks authority on every request. If the agent proposes a refund, a reviewer approves that invoice, recipient and amount. Change any of them and the approval no longer applies.

Now the ticket contains a line telling the agent to email billing records to an outside address. That line is untrusted data and grants nothing. Even if prompt injection convinces the model to try, a destination rule enforced outside the model denies the export.

Authorization limits what a manipulated agent can do. Permitted actions can still be wrong, so sensitive results still get checked.

How should teams scope and enforce agent permissions?

Apply least privilege to the operation, resource, destination and duration. Write the task boundary first, then map it to permissions. Broad OAuth scopes or service roles usually need tighter limits in the application or the tool server.

Enforce outside the model. A system prompt saying 'never delete files' is a request to the model, and nothing enforces it. Removing a delete tool from an MCP server doesn't help if a coding agent can still run rm in a shell.

Split read, write, delete, send and permission changes.
Scope access to the customer, repository, folder, mailbox or tenant the task needs.
Allow-list export destinations and check the target before execution.
Use short-lived, task-scoped credentials where supported. Keep secrets out of the model's context.
Deny the request when identity, resource or approval context is missing.
Test forbidden actions through every path, including alternate tools.

When should an agent need human approval?

When the consequences justify the pause. Deleting production data, changing access rights, sending sensitive files outside the company. Routine reads inside a narrow policy can run on their own.

Human approval works when the reviewer sees the exact target, the proposed change and what will happen. Bind the approval to those details. 'Go ahead and finish the task' doesn't approve everything that comes after it.

Recheck authorization at execution time, because permissions and resources change after review. Log the decision and the result separately so an approved proposal isn't mistaken for a completed action.

How do you revoke an agent's access and confirm it stopped?

Killing the agent process can leave tokens, sessions and scheduled jobs alive. Revoke the grants, issued tokens, active sessions and anything delegated downstream. Revoking a refresh token doesn't necessarily invalidate existing access tokens immediately, as the OAuth token revocation spec explains.

An AI agent kill switch should stop execution and pull authority at the same time. Then send a request and confirm it fails, including from sub-agents. Keep the record of agent, user, target, decision and result.

Review access when a task ends, an owner changes or the workflow gains tools. Revocation doesn't undo changes already made or bring back data already sent.

How Identra thinks about it

Identra finds AI agents and their owners across Microsoft 365 Copilot and Foundry, Google Workspace and Anthropic, and collects OAuth grants with the app, permissions and who granted them. Analysts can revoke risky grants, and the result is recorded. On endpoints, destructive shell commands from AI agents can be denied by policy, and each agent run is logged with the user, device and whether it was allowed or blocked.

Go deeper: AI security, built on identity

Frequently asked questions

Are OAuth scopes enough for AI agent authorization?

Often not. A scope can cover far more resources and operations than the task needs. Add resource limits and action-level policy where it does.

Can a system prompt enforce authorization?

No. It can guide behavior. Access limits have to be enforced outside the model, where the agent can't route around them with another tool or credential.

Should an agent inherit all of a user's permissions?

No. Delegate what the task needs, even if the user has more. Keep both the user and the agent in the action record.

Does every tool call need human approval?

No. Every protected operation needs an authorization decision. Narrow, routine actions can run automatically. Save human review for actions with real consequences.

Does authorization prevent prompt injection?

It doesn't stop the model from following a malicious instruction. It can deny the resulting request when it goes beyond the agent's allowed actions, resources or destinations.

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