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AI agents need identities and authorization controls that distinguish the agent from the person or service behind a request. The most common failures are shared credentials, exposed or long-lived secrets, excessive tool permissions, unclear delegation, and treating a model’s response as permission to act. Give each agent a distinct identity, limit and protect its credentials, and enforce policy outside the model before consequential actions run.
Why does an AI agent need its own identity?
An agent that calls tools, reads enterprise data, or acts for a person can exercise real authority. Its identity should make it possible to tell which agent acted, for whom it acted, and what the agent was permitted to do. Those are related but separate questions.
- Authentication establishes which principal is presenting a credential: for example, a particular agent workload or a user.
- Authorization decides whether that principal may perform a particular action on a particular resource, under the current conditions.
- Approval, when required, confirms that a person or other authorized process accepted a specific consequential action. A valid identity does not itself provide that approval.
A language model’s confidence, a user’s prompt, or the fact that a tool is available does not answer the authorization question. The tool gateway or target service must enforce identity, policy, scope, and any required approval independently of the model.
Which authentication failures are most common, and how should teams fix them?
1. The agent uses a person’s password or session
If a user gives an agent their account password, API token, or session credential, downstream systems may record the user as the actor, not a distinct agent. That makes it harder to determine whether an action was performed directly by the person or by software, and complicates investigation and accountability.
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Fix: Assign the agent a distinct workload or agent identity. When it acts on a person’s behalf, use a supported delegated-authorization flow that preserves both the agent identity and the user whose authority is being delegated. Do not assume every consumer service supports this model; the available flow depends on the target service and deployment.
2. Static secrets or bearer tokens can be copied and reused
A static API key or bearer token is a transferable secret: someone who obtains it may be able to present it. NIST’s Cybersecurity Insights discussion of agent identity highlights the risks of static and long-lived credentials, including secrets left in configuration files, Markdown, or logs.
Fix: Keep credentials out of prompts, retrieved content, source control, and ordinary logs. Use a managed secret store or credential broker where appropriate, grant only the necessary scope, and establish a tested rotation and revocation path. Prefer short credential lifetimes where the platform supports them; after suspected exposure, revoke or replace the affected credential and investigate its use. Proof-of-possession or token-binding mechanisms can reduce replay risk where both the platform and target support them, but they are not universal.
3. A tool has more permission than the task requires
A narrowly worded prompt cannot compensate for a tool configured with broad write access or administrative privileges. If an agent can invoke a tool, its actions still need to be constrained at the tool and resource level.
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Fix: Give each tool the minimum permissions needed for its job. Prefer read-only access when writing is unnecessary, and scope access to specific resources rather than using wildcard permissions. Separate tools or identities with different trust levels. Enforce these boundaries at the gateway or service boundary, not by asking the model to respect them. OWASP’s AI Agent Security Cheat Sheet recommends minimum necessary tools, per-tool scoping, and explicit authorization for sensitive operations.
4. Delegated authority is vague or lasts too long
An agent may act under its own machine authority, under a named user’s delegated authority, or through a combination of the two. If that distinction is unclear, teams can lose track of who authorized access, which resources were intended, and when delegated access should end. Aggregating information from multiple sources can also exceed what a user intended to authorize.
Fix: Make delegation explicit and scoped where the service supports it. Record both the agent and the delegating user, limit access to the resources and actions the task requires, and provide a clear way to revoke the grant. Review whether combined or derived access remains within the intended scope. NIST’s February 5, 2026 concept paper, Accelerating the Adoption of Software and AI Agent Identity and Authorization, identifies delegation, human-agent binding, and changing context as design questions; it presents a proposed effort and seeks community input, not a universal delegation scheme.
5. Prompt injection turns legitimate tool access into an unsafe action
Untrusted text from a document, webpage, email, or other source may attempt to redirect an agent into misusing a tool or disclosing data. The agent’s tool call may be authenticated and still be inappropriate for the actual actor, target, or circumstances.
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Fix: Separate the model’s proposal from execution for financial, administrative, irreversible, or externally visible actions. An independent policy or execution component should validate the actor, tool, target, normalized parameters, current authorization, approval status, time bounds, and replay state before the action runs. Use action-bound approval so confirmation applies to the actual operation and its parameters, not merely to a broad task description. Consider step-up authentication for critical actions, use idempotency controls where practical, and fail closed if a required policy, approval, or audit check is unavailable. These controls align with recommendations in OWASP’s AI Agent Security Cheat Sheet.
6. Logs cannot establish what happened, or expose secrets
Logs that identify only a user, or only a generic service account, may not show which agent used which tool on which resource. Conversely, recording full prompts, payloads, or credentials can create another sensitive-data exposure.
Fix: Keep structured decision records sufficient to reconstruct who or what acted, for whom, which tool and resource were involved, what authorization applied, and whether approval was present. Do not log raw credentials, and avoid retaining sensitive payloads unless there is a justified need and appropriate protection. Include relevant policy and decision identifiers so an investigation can connect an action to the rule that permitted or blocked it.
7. Credentials and grants remain after an agent is retired
Deleting an agent runtime or resource does not necessarily remove every credential, role binding, or access grant associated with it. Google Cloud’s Agent Identity documentation, for example, says IAM bindings associated with its agent resource can remain after that resource is deleted and must be removed separately. That is a Google Cloud-specific operational detail, not a universal rule.
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Fix: Treat identity lifecycle as part of deployment and decommissioning. Review identity creation, scope changes, credential renewal, rotation, revocation, and deletion. When retiring an agent, remove its credentials and stale grants at the identity provider and target services, and verify that the access is no longer usable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should authorization and approval work in an agent workflow?
Put a policy-enforcement point between model-generated intent and tool execution. The model can suggest an action; the enforcement layer decides whether that action may proceed. At minimum, evaluate:
- the authenticated agent and, where relevant, the user or service whose authority it represents;
- the requested tool, resource, operation, and normalized parameters;
- the identity’s current scope and applicable policy;
- whether the operation requires human approval or stronger authentication;
- whether approval matches this exact action and remains valid;
- whether the request is stale, duplicated, or a replay; and
- whether the decision can be recorded without exposing credentials or unnecessary sensitive data.
Do not treat a prompt that says “the user approved this” as proof of approval. Approval should come through a trusted channel and be bound to the operation that will actually execute. If required checks or approvals cannot be verified, do not execute the action.
Which identity mechanisms should teams evaluate?
NIST identifies SPIFFE and OAuth 2.0 as existing mechanisms relevant to enterprise agent identification and authorization, while noting that approaches continue to evolve. They solve different deployment needs; neither label by itself guarantees least privilege, safe delegation, or correct tool enforcement.
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- OAuth-based authorization: Check how credentials are issued, scoped, refreshed, expired, and revoked; whether delegation is supported; and whether target services preserve the user-agent relationship in their records. RFC 9700, published by the IETF in January 2025, is the Best Current Practice for OAuth 2.0 security and a useful standards reference. Apply the current protocol documentation and the target provider’s requirements rather than assuming one configuration fits every service.
- Agent-specific managed identity: Google Cloud’s Agent Identity documentation describes a vendor-specific implementation using SPIFFE-based agent identities, managed X.509 certificates, mTLS for certain Google Cloud API communication, delegated and machine-to-machine OAuth options, IAM policy controls, and audit attribution. It documents certificates with a 24-hour validity period that are automatically refreshed. These details apply to the documented Google Cloud services; they should not be assumed for other runtimes or providers. Google documents HTTP basic authentication as not recommended.
Compare candidate approaches against identity isolation and lifecycle binding; credential scope, expiry, rotation, and revocation; delegation and downstream attribution; authorization granularity; replay resistance where supported; independent policy enforcement and approval; audit quality; and operational fit across the runtime and target services.
Quick Recap
What should teams verify before deployment?
- Map the principals: List each agent identity, its runtime, the tools it can call, and any user or service authority it may receive. Confirm that an agent is distinguishable from the person delegating work.
- Trace credentials end to end: Identify where credentials are issued, stored, presented, renewed, logged, and revoked. Check prompts, retrieved content, configuration, source control, and logs for accidental exposure.
- Test least privilege: For each tool, verify allowed operations and resources. Attempt an out-of-scope read, write, and administrative action and confirm the enforcement layer denies them.
- Test delegation and approval: Verify that the intended user-agent relationship appears in records, that delegation can be revoked, and that an approval for one action cannot authorize a materially different action.
- Exercise failure paths: Test expired or revoked credentials, unavailable policy and approval services, duplicate requests, and adversarial content that tries to redirect tool use. Confirm sensitive actions fail closed when mandatory checks are absent.
- Test retirement: Delete or disable a test agent, then verify that credentials and grants at every relevant provider and target service have also been removed.
- Review records: Confirm that an operator can reconstruct the actor, delegated user if any, tool, resource, authorization decision, and approval status without retrieving raw secrets.
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