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Start with what the agent can do
IAM matters most when an agent can reach tools, data, or services and take actions—not merely when a model generates text. The NIST National Cybersecurity Center of Excellence (NCCoE) concept paper describes agents that receive instructions, gather context from resources, process it, and may act. It focuses on agent identification, authorization, delegation, auditing, and reducing the impact of prompt injection. It excludes retrieval-augmented generation architectures and systems that only use an LLM with its associated training data.
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That distinction helps define your selection scope. If an agent can call an API, change a record, send a message, or otherwise act beyond producing an answer, it needs an identity and permissions that let your organization govern and review those actions.
How do I choose an identity and access management system for AI agents?
Use your actual agent workflows to test a shortlist. For each workflow, document what the agent does, which resources it touches, whose authority it uses, and what should happen when that authority expires or must be withdrawn. Then evaluate each IAM system against the controls below rather than relying on a general claim of “AI agent support.”
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1. Confirm each agent has an identifiable owner
Ask whether the system can distinguish an agent identity from a human user, ordinary application, or other workload. Check which metadata is associated with the identity, whether agents can be organized by blueprint or class, and how an accountable owner or sponsor is assigned. Where authority is delegated, the record should retain the link to the user or system that enabled it.
Do not use shared human credentials as a shortcut. NIST warns that credential sharing creates accountability gaps; its August 27, 2026, guidance on agent identity states, “Credential sharing is a bad idea in all contexts.” A shared login makes it harder to establish which agent or person initiated an action.
2. Test the credential lifecycle, not just sign-in
Find out how credentials are issued, protected, rotated, verified, and revoked. Ask how short-lived credentials work for ephemeral agents, how quickly revocation takes effect, and whether tokens are restricted by audience and scope. Avoid making exposed static API keys, long-lived bearer tokens, or shared passwords the default.
A bearer token or static key can be used by anyone who obtains it, and may grant access too broadly. NIST’s NISTIR 8587, published September 15, 2026, provides implementation recommendations for protecting tokens and assertions, including key management, verification, and lifecycle controls. Ask vendors to demonstrate the relevant controls in the product and edition you would deploy; a general standards claim does not establish how a particular configuration behaves.
3. Match authorization to the agent’s operating pattern
Separate interactive agents from autonomous ones in your requirements. An interactive agent may act on behalf of a signed-in user through delegated permissions. An autonomous agent may authenticate using its own identity. These models have different authorization needs, and your IAM system should support the one you actually plan to use.
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For either model, check whether permissions can be limited to the specific tools, data, and actions the task requires. Ask whether policy can respond to context changes, whether the system can prove what authority was delegated, and whether a high-impact action can be held for human approval. The NCCoE concept paper raises least privilege, dynamic policy, delegated “on behalf of” access, authority proof, and human-in-the-loop binding as design questions; it is exploratory, not a finalized universal checklist.
4. Check whether the audit trail supports investigation
Require records that let your operators determine which agent acted, what it attempted, what authority it used, and which user or system enabled that authority. Verify coverage across both the IAM control plane and connected tools. Also check retention, export, review workflows, and how quickly operators can disable one identity or a group of identities.
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NIST identifies tamper-resistant, verifiable records and non-repudiation as important design areas. A log entry in the IAM product alone may not reconstruct what happened in a connected service, so establish which systems record each part of the activity and how those records can be correlated.
5. Verify standards and integration fit
Compare support for the standards and protocols your organization actually uses, including OAuth 2.0, workload identity approaches such as SPIFFE, API authorization, and the agent or tool protocols in your deployment. Confirm integration with your current identity provider, cloud platforms, applications, and security monitoring.
NIST identifies OAuth 2.0 and SPIFFE as foundations for enterprise agent identification and authorization. It also points to emerging work, including Workload Identity in Multi-System Environments (WIMSE) and the Identity Assertion JWT Authorization Grant. Treat those as developing standards work, not evidence that every IAM product implements them interoperably. Confirm protocol versions, product tiers, regional availability, and configuration requirements with each vendor.
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6. Include ownership and operations in the evaluation
Evaluate how the product helps you discover agents—including shadow or forgotten agents—assign owners, review access, manage entitlements, expire and retire identities, and respond to incidents. Consider how IAM coordinates with network controls and tool gateways. Identity controls can limit the authority an agent has, but do not ensure that it interprets instructions safely or prevent prompt injection by themselves.
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Score each candidate against the agent types and risk tolerance in your organization. Record what you verified in a working configuration, what depends on another product, and what remains unconfirmed. The axes below help keep comparisons focused on operational requirements rather than feature names.
| Comparison axis | Questions to answer |
|---|---|
| Identity coverage and ownership | Can every agent be uniquely identified, classified, and assigned an accountable owner? Can delegated authority be traced to its user or system? |
| Credential protection and lifecycle | How are credentials issued, protected, scoped, rotated, expired, and revoked? How quickly does revocation propagate? |
| Delegation and least privilege | Can permissions be limited to the task, tool, and data? Does the system support your interactive or autonomous pattern and any required human approval? |
| Auditability and containment | Can you reconstruct the agent’s activity and authority across connected tools? Can you disable an individual identity or class of identities? |
| Standards and integration fit | Does the product work with your identity provider, workloads, APIs, protocols, and monitoring environment? Are the required versions supported? |
| Governance and operating effort | Can teams discover, assign, review, expire, and retire identities? What deployment, edition, geography, and configuration constraints apply? |
For every claimed control, check the current product documentation and confirm that it is available in the edition, region, and deployment you intend to use. If a control relies on a separate service or integration, include that dependency in the comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should an AI agent have its own identity?
An agent that can take actions should generally be identifiable as an agent rather than borrowing a person’s credentials. Its identity should make it possible to determine who owns it and, when it acts with delegated authority, which user or system authorized that authority. This supports accountability and makes it easier to scope or withdraw access.
That does not mean every agent must operate autonomously. A user-facing agent can use a delegated, “on behalf of” model; an autonomous service can use its own workload identity. The key requirement is that the identity and permissions match the operating model and preserve the authority trail.
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How do I control what an AI agent can access?
Start with the agent’s task and enumerate the resources and actions it needs. Grant the narrowest permissions that let it complete that task, restrict credentials to their intended audience and scope where supported, and use policy or approval gates for sensitive actions. Then verify that logs cover the agent’s requests and the connected services that carry them out.
Review the permissions as the task, owner, or agent changes; set expiry and revocation behavior; and make sure operators know how to disable access during an incident. IAM is one layer in this design: use it alongside controls for tools, data, monitoring, and reducing the impact of prompt injection.
Can an AI agent act on behalf of a user without sharing the user’s password?
Yes. A delegated authorization model can let an interactive agent act for a signed-in user without giving the agent the user’s password. The user’s authority is delegated through the identity system, and the resulting access should be limited to the permissions needed for the task. Test how the system represents that delegation, scopes it, logs it, and withdraws it.
What current guidance does—and does not—establish
NIST is adapting existing identity standards and practices for agents rather than prescribing a completed, universal agent IAM specification. Its February 5, 2026, concept paper says organizations need to understand how identification, authentication, and authorization can help ensure agents are known, trusted, and governed. The NCCoE project resource hub describes a practice guide in development, intended to include example implementations, architectures, build details, and lab lessons. The hub reports more than 600 responses to the concept paper; that is a participation count, not evidence of product effectiveness or market adoption.
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NIST SP 800-63-4 is useful context for digital identity, but it primarily addresses user identity proofing, authentication, and federation. It does not cover some machine-to-machine authentication and API access scenarios, so it is not a complete agent IAM specification. See the NIST SP 800-63-4 Digital Identity Guidelines for its scope.
Microsoft Entra Agent ID as a feature checklist, not a winner
Microsoft’s documentation describes Entra Agent ID features including agent registration and management, identity metadata, agent-to-agent discovery and authorization using protocols such as MCP and A2A, activity logging, conditional access and risk controls, lifecycle governance, access reviews, and time-bound access packages. It describes delegated permissions for interactive agents and an agent identity for autonomous agents. These are vendor-described capabilities, not an independent assessment or proof that every feature is included in every license or deployment.
Use the example to form questions for any candidate, then verify current packaging and behavior in the relevant environment. Microsoft Learn’s page was last updated May 8, 2026: Microsoft Entra security for AI overview – Microsoft Entra Agent ID.
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