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Any AI agent that accesses enterprise data or takes actions needs an accountable identity—but identity alone does not make its actions safe or authorized. A secure design identifies what is acting, limits what it can access and do, assigns a human owner, and monitors whether policy is being followed.
What an agent identity does—and what it does not do
An AI agent identity is a principal that an organization can authenticate, authorize, govern, and audit. It answers who or what is acting? That makes it possible to apply policy to the agent and connect activity to an accountable owner.
Identity is not permission. A principal may be authenticated and still have no right to access a particular dataset, call a particular tool, or perform a particular operation. Nor does an identity prove that an action is safe or that the agent interpreted a request correctly. The control model has three separate parts:
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- Authorization: the data, APIs, and tools that principal is allowed to access.
- Action control: whether this specific operation is allowed in this context, and whether it needs additional approval.
Microsoft’s guidance on least privilege for AI agents describes the risks of ambiguous identities, permission creep, and tools with overly broad access. A stable identity makes those problems manageable; it does not solve them by itself.
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Choose an identity pattern that matches how the agent operates
Not every agent needs the same authentication flow. Microsoft distinguishes between agents acting for a signed-in person and autonomous agents acting independently. This is Microsoft’s platform guidance, not a universal protocol mandate; available features and implementation details vary by platform.
| Operating pattern | Identity and authentication pattern | Authorization question |
|---|---|---|
| Interactive: acts on behalf of a signed-in person | Microsoft describes delegated permissions and the on-behalf-of flow. | Which of the user’s permissions should the agent be allowed to exercise for this task? |
| Autonomous: acts independently of a signed-in person | Microsoft describes the agent using its own identity with the client credentials flow. | Which application permissions, resources, and operations does the agent actually need? |
Do not blur these patterns by quietly combining a person’s credentials, a shared service account, and autonomous behavior. That makes it harder to determine whose authority was used and who should answer for an action. If delegated access is sufficient, Microsoft recommends avoiding application permissions; when application permissions are needed, limit them to the required resources.
Before choosing a pattern, ask: Is the agent operating for a signed-in user, or independently? What tenant, data, APIs, tools, and actions must it reach? Which operations are consequential enough to require fresh human approval? These questions should have explicit answers before credentials or permissions are issued.
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Build controls around the principal, permissions, and actions
Make access narrow and enforceable
Grant the minimum permissions required for the agent’s purpose, and constrain its tools to approved targets and scopes. Authorization should apply to tool calls and cross-boundary delegation, not just to the agent’s initial sign-in. For high-impact or irreversible operations, consider task-limited privilege and an approval step rather than giving the agent standing authority to act broadly.
Agent identities also need policies suited to agents. Microsoft notes that an autonomous agent may not have the conventional human signals that policies rely on, such as interactive multifactor authentication, a user’s device, or location context. Microsoft recommends agent-aware policy conditions and classification metadata rather than assuming policies written for people will fit an autonomous workload.
Protect credentials for the chosen flow
For production deployments, Microsoft recommends managed identities or certificates, where supported, rather than relying on reusable client secrets. Store credentials securely, isolate them by environment or blueprint, and select an authentication flow that matches how the agent operates. A credential approach that is available in one platform or deployment is not necessarily available in another.
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After deployment, check sign-in logs to confirm the intended principal and flow are being used. A successful sign-in is not proof that access is properly scoped; review the permissions and the resources the agent can reach separately.
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Assign a human sponsor and a technical owner. The sponsor is accountable for the agent’s business purpose and continued need; the technical owner maintains its configuration and controls. Record both alongside the agent’s environment, data sensitivity, tools, and operating pattern.
Microsoft’s guidance covers inventory and discovery, ownership, access reviews, expiration, deactivation, and decommissioning. It suggests sponsor attestations every 6–12 months; that is vendor guidance, not an industry-wide required interval. Set a review cadence based on organizational policy and the agent’s risk, and make sure an agent can be disabled and its access revoked when it no longer has a valid purpose or owner.
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Implement the controls in a practical sequence
- Inventory and classify. Find agents across platforms and hosting environments, including deployments outside the central AI platform. Record purpose, owning team, environment, data sensitivity, tools, and whether each agent acts for a user or autonomously. Microsoft describes centralized discovery and metadata as ways to reduce shadow AI.
- Select the principal model. Choose delegated user authority for an interactive agent or its own workload or agent identity for an autonomous one, according to the platform and use case. Document why the selected model fits.
- Assign ownership and configure authentication. Name a human sponsor and technical owner. Choose a supported production credential option, isolate credentials between environments, and validate the sign-in flow in logs.
- Scope permissions and tools. List the required resources and actions, remove unnecessary access, and constrain tool targets. Decide which consequential operations need approval at the point of action.
- Apply agent-aware policy. Use policy conditions and classification that account for an agent’s operating pattern; do not assume human-user controls supply signals an autonomous agent does not have.
- Connect monitoring to response. Log and review token acquisition, activity, permission grants, configuration changes, and credential changes. Define how alerts lead to blocking, containment, or revocation.
- Review, expire, and retire. Reassess the sponsor, purpose, and access on a defined cadence. Expire or decommission agents that are no longer needed, and revoke their credentials and permissions.
Monitoring must be able to stop access
Logs provide evidence; risk scores help identify suspicious activity. Neither prevents an agent from obtaining or using access unless an enforcement policy acts on the signal. Microsoft’s zero-trust guidance explains that a policy must consume a high-risk signal and block token issuance to enforce that finding. Organizations should define the corresponding containment and revocation steps as part of incident response, not leave a high-risk alert as a record to inspect later.
Identity and authorization can limit the impact of an agent’s behavior, but they do not prevent prompt injection or guarantee that the agent will interpret instructions correctly. Treat those as separate risks. Keep permissions narrow, constrain actions, and require approval where the consequences justify it.
What NIST’s current work does—and does not—establish
NIST’s NCCoE project on Software and AI Agent Identity and Authorization is soliciting comments on a rolling basis, with input informing project planning. NIST announced its concept paper on February 5, 2026. These materials describe active work, not a finalized general agent-identity standard, binding regulation, certification, or completed implementation profile.
The concept paper raises practical questions for security teams: “How might agents be identified in an enterprise architecture?” “What constitutes a strong authentication for an AI agent?” and “How do we establish ‘least privilege’ for an agent, especially when its required actions might not be fully predictable when deployed?” Those are useful design questions, but the current project status should not be mistaken for a settled universal answer.
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