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Enterprise AI agents that access company data or take actions need an accountable identity: a principal that lets an organization authenticate the agent, attach policy to it, and trace its activity to an owner. That identity is a foundation for security, not permission to do anything. Access to each resource and approval for each consequential action still need separate controls.
What an agent identity does—and what it does not
An identity answers who or what is making a request. In an enterprise, it should be distinguishable from a human user’s identity and from other agents, so access policy and activity records can refer to the right principal. A human sponsor or technical owner remains accountable for the deployment; the identity itself is not a substitute for human ownership.
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Three decisions must stay separate:
- Identity: Which user or agent is acting, and how is that principal authenticated?
- Authorization: Which data, APIs, or other resources may that principal access?
- Action control: Which particular operation may it perform, on which target, and when should approval be required?
A unique identity makes authorization and auditing more precise, but it does not make a request safe or authorized by itself. Microsoft’s Least privilege for AI agents (agentic identities + RBAC) identifies identity ambiguity, permission creep, and overly broad tool access as risks. Giving an agent a broad application permission or an unrestricted tool simply under a unique name can still produce excessive access.
Choose an identity pattern that matches how the agent operates
The key question is whether the agent acts in the context of a signed-in person or independently. Microsoft Entra documents different patterns for these cases. They are platform-specific guidance, not a universal mandate that every agent use the same protocol or configuration.
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| Operating pattern | Authority and authentication pattern | What to constrain |
|---|---|---|
| Interactive: agent acts on behalf of a signed-in user | Delegated permissions; Microsoft describes the on-behalf-of flow. | Keep the agent’s access within the user’s authority and the task’s required resources and actions. |
| Autonomous: agent acts independently of a signed-in user | The agent uses its own identity; Microsoft describes the client credentials flow. | Grant only the application permissions and tool access needed for its defined work, with an owner and review process. |
Microsoft recommends avoiding application permissions when delegated permissions are sufficient. Conversely, an autonomous workload may need an identity and permissions of its own rather than an implicitly reused human credential. Do not blur the two patterns by combining a shared service account, human credentials, and autonomous behavior in a way that makes the authority behind an action unclear.
Decide what authority the agent actually needs
Before provisioning credentials, map the agent’s job to resources and actions. A request such as “help with customer support” is not an authorization boundary. Specify the data, APIs, tenant or environment, tools, and operations needed for the actual workflow. Where the platform supports it, prefer narrow, task-limited access over standing broad permissions; consider fresh approval for high-impact or irreversible actions.
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Tool calls are authorization decisions, too. An agent that can read a record does not necessarily need to alter it, export an entire dataset, or invoke every tool available to the platform. Define allowed tools and targets, then enforce those limits in the authorization layer rather than relying only on the agent’s instructions to behave appropriately. NIST’s concept paper raises the practical challenge directly: how to establish least privilege when an agent’s required actions may not be fully predictable at deployment.
Implement identity as a security control, step by step
- Inventory and classify agents. Find agents across platforms and hosting environments, including deployments outside a central AI platform. Record each agent’s purpose, environment, owning team, data sensitivity, available tools, and whether it acts for a user or autonomously. Microsoft Entra security guidance describes centralized discovery and metadata as ways to reduce shadow AI.
- Select the principal model. Choose delegated user authority for an interactive agent acting on behalf of a signed-in person, or a separate workload/agent identity for an autonomous one. Document the decision and the authority the agent is expected to exercise.
- Name an accountable owner and secure authentication. Assign a human sponsor as well as a technical owner. For production, Microsoft recommends managed identities or certificates where supported, secure key storage, and credential isolation by blueprint or environment. Choose an authentication flow appropriate to the operating pattern; avoid assuming that a stored reusable secret is the only option or that every platform supports the same alternatives.
- Limit permissions and tools. Grant only the permissions required for the relevant resources and APIs. Keep allowed actions and targets explicit, and add an approval step when the operation’s impact warrants it. Microsoft’s best-practice guidance recommends least privilege and limiting access to required resources.
- Apply policies designed for agents. Do not assume that policies written for human users automatically fit autonomous agents. Microsoft notes that agents may not provide conventional interactive-user signals such as MFA, device, or location context. Use agent-aware policy conditions and classification metadata where available, and make sure the policy is meaningful for the authentication flow in use.
- Monitor activity and enforce responses. Log and review token acquisition, agent activity, configuration changes, credential changes, and permission grants. Connect risk detections to a defined response—such as blocking token issuance, containment, or revocation—rather than treating a log entry or risk score as protection on its own. Microsoft’s Zero Trust guidance explains that enforcement requires a policy that consumes the risk signal and blocks access.
- Review, expire, or retire access. Schedule sponsor and access reviews, remove permissions no longer needed, and deactivate or decommission agents that lack a current purpose or owner. Microsoft suggests sponsor attestations every 6–12 months; this is vendor guidance, not a universal industry requirement. Set the review cadence according to organizational policy and the agent’s risk.
Build accountability into the lifecycle
Ownership is not just a field in an inventory. Someone must be responsible for approving the agent’s purpose, confirming that its access remains justified, responding to suspicious activity, and arranging retirement when the agent is no longer needed. Microsoft Entra guidance covers governance from deployment through expiration, including sponsors and owners, inventory, access review, deactivation, and decommissioning.
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Keep the lifecycle record useful to operators: tie the principal to its owner, environment, purpose, permissions, credential approach, and review date. When an agent changes teams, tools, or role, reassess its access rather than carrying forward permissions from its previous task. When its owner or business purpose disappears, ensure there is a process to disable credentials and revoke access.
Identity limits the blast radius; it does not solve every agent threat
Identity and authorization can help limit what an agent can reach or do, and make its activity attributable. They do not guarantee that the agent will interpret instructions correctly, resist prompt injection, or make sound decisions. A compromised or manipulated agent may still misuse whatever authorized tools and data it has. Separate controls for agent behavior, tool execution, data protection, and human review remain necessary for the risks they address.
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Likewise, collecting logs is not the same as stopping access. Detection must connect to an enforceable policy or response path if the goal is to block a risky token request, contain an agent, or revoke its ability to act.
What NIST’s current work does—and does not—establish
NIST announced a concept paper on identity and authority of software agents on February 5, 2026. The NCCoE project page describes work on “Software and AI Agent Identity and Authorization” and says it is soliciting comments on a rolling basis, with input informing project planning. That status is active project work, not a finalized general-purpose agent identity standard, binding regulation, certification, or completed implementation profile.
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The NCCoE concept paper frames questions enterprises should consider, including how agents are identified in an enterprise architecture and what constitutes strong authentication for an AI agent. Those questions reinforce the need to define principals, authority, and accountability; they do not prescribe a single deployment model. Microsoft’s interactive-versus-autonomous patterns are useful implementation guidance for Microsoft Entra environments, while organizations should verify the capabilities and controls of their own platforms.
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