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Inventory an AI agent as a distinct nonhuman identity whenever it can choose tools, access data, invoke services or take consequential action. The record should identify the actor, its accountable owner, how it authenticates, what authority it has, what it can reach and what it did—including which person or organization authorized the task. Do not collapse the model, application, running workload, agent, tool credential and target resource into one identity or shared service account.
The practical objective is not to label every model call an agent. It is to discover agent-like activity, classify its authority and autonomy, and connect each action to its origin and outcome. NIST’s agent-identity work emphasizes identification, authentication, authorization, delegation, logging and data-flow provenance; the taxonomy and controls below turn those concerns into an inventory you can operate.
What counts as an AI agent identity?
For inventory purposes, treat an AI agent as a software system that receives instructions, dynamically acquires context, processes it, may select tools or services, and may act with limited human supervision. The security question is whether the software can exercise authority—not whether a vendor calls it an “agent.” An assistant that only drafts a response for a person to review has a different risk profile from one that can send messages, change records, approve transactions or delete data.
NIST’s NCCoE concept paper focuses on this kind of agentic architecture. It explicitly places retrieval-augmented generation and LLM-only systems outside that particular project’s scope. That is a scope choice for the project, not a claim that retrieval systems never create security risk. An organization should define its own inclusion boundary based on actual capabilities: include a system when it can select or invoke tools, access protected data, or take action under delegated authority.
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Keep these components distinct in the inventory:
- Model: The reasoning or generation component. It can influence a decision, but it should not be treated as the security boundary.
- Application: The product or backend that presents the experience and connects to identity, policy and tools.
- Workload: The process, container, function or service running the software.
- Agent: The governed actor that plans or executes a task, potentially across multiple steps.
- Tool identity: The authorization context a connector or tool uses to make a call.
- Resource identity: The API, database, SaaS system, storage location or business application being accessed.
- Task originator or delegator: The human, service, organization or upstream agent that starts or passes along the work.
These identities may be related, but they are not interchangeable. Microsoft Learn recommends keeping user, application, workload, managed, agent, tool and resource identities conceptually separate. If an agent and its connector share a broad service account, an audit may show which account acted without showing which agent, task or human caused the action. The same collapse can make it difficult to apply different permissions to reading a record and changing it.
How do I discover AI agents and agent-like identities?
Start with an organization-wide discovery sweep rather than assuming an AI registry is complete. Agents and their credentials may be created or configured in identity, cloud, development, integration and runtime systems, while the AI registry—if one exists—may only contain declared deployments.
- Search identity directories and cloud IAM. Review workforce and service-account directories, application registrations, managed identities, OAuth grants and cloud-native workload identities. Look for identities used by AI applications, connectors, automation and service processes, but do not classify an account as an agent solely because its name includes “AI.”
- Inspect execution and delivery systems. Check Kubernetes and other workload-identity systems, CI/CD pipelines, application deployment records and runtime configuration. Record the workload and environment that run an agent, not only the application registration associated with it.
- Review credentials and trust paths. Examine secrets stores, certificate authorities, API gateways and token-issuance or federation configuration. Identify which actor each credential is meant to represent, which system can obtain it and whether it is static, reusable or temporary.
- Map tool and SaaS connections. Review agent and tool registries, MCP or connector configurations, SaaS integrations and API permissions. Trace each connector to the identity it uses and to the resources and actions that identity can reach.
- Use logs to find undeclared activity. Compare configuration with runtime and audit logs. Look for tool calls, service-account activity or access patterns that do not map to an owned, approved inventory record. NIST’s concept paper calls for identifying software and AI systems, linking user identities to agents, logging actions and tracking prompt and data-input provenance.
- Reconcile, deduplicate and assign ownership. Join records from different systems without erasing their distinctions. A workload, agent and credential may have separate records linked to one another. Send unresolved records to an accountable team, and treat unowned or unexplained access as a governance issue rather than silently assigning it to the nearest application owner.
Useful discovery sources include identity directories, cloud IAM, managed-identity services, application registrations, OAuth grants, API gateways, secrets stores, certificate authorities, workload-identity systems, CI/CD, agent and tool registries, connector configuration, SaaS integrations, and runtime or audit logs. No single source should be assumed to reveal every agent, its active credentials and its effective reach.
What should an AI-agent inventory record contain?
A useful record ties an identifiable actor to an owner, a runtime, a mandate, its authentication and its effective access. Capture the identity subject separately from the authentication mechanism: the subject is the actor; a secret, token, certificate or temporary credential is how that actor proves legitimacy.
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| Record area | Minimum fields to capture | Why it matters |
|---|---|---|
| Identity and ownership | Stable agent identifier; human-readable name; accountable owner and team; business purpose; incident contact; lifecycle state. | Lets staff distinguish the actor from similarly named applications or workloads and identify who can explain, suspend or retire it. |
| Application and runtime | Host application; workload identity; runtime; environment; deployment location; approved configuration; relevant model or model-service metadata. | Connects the logical agent to the software and environment actually executing it. Model metadata is useful context, not a substitute for an identity boundary. |
| Task and delegation | Originator or delegator; mandate; intended outcome; constraints; downstream agents, services and recipients; maximum delegated scope. | Preserves the chain from the human or organization that authorized work through downstream execution. |
| Authentication | Agent, application, workload and tool identities; credential mechanism; issuance, expiry, rotation and revocation details. | Separates who is acting from the credential used, and gives responders a way to identify and revoke access. |
| Access and data | Tools and resources reachable; read, create, modify, send, approve, delete, transfer or execute permissions; data classifications; environment and trust boundaries. | Makes the agent’s effective authority reviewable, including whether a connector has broader access than a task needs. |
| Policy and oversight | Entitlements; policy decisions; approval thresholds; separation-of-duties constraints; expiry; human-review requirements. | Shows which controls constrain the agent and when a person must review an action. |
| Provenance and operations | Relevant prompt and data-source provenance; activity logs; last-seen time; anomalous actions; review history; suspension and decommissioning evidence. | Supports risk decisions, incident investigation and verification that an identity was actually retired. |
The fields need not live in one database. An inventory can link to authoritative identity, workload, credential and resource systems, provided the links are stable and preserve which record describes which subject. A display name alone is not a stable identifier: names can change or collide. Likewise, a credential identifier is not the agent’s identity. One subject may use multiple authentication mechanisms over its lifecycle, while a credential may be rotated without creating a new actor.
How should I classify AI agents as nonhuman identities?
Use several classification axes rather than a single “agent” label. The axes below help security and governance teams derive policy from what the actor can do, how it obtains authority and what risk it can encounter.
- Identity subject. Use a subject category such as service or integration account, workload or microservice, infrastructure, device or agent identity. Record an agent identity distinctly where the system can plan or execute tasks; do not confuse it with the workload or integration account supporting it. The Cloud Security Alliance (CSA) taxonomy treats agents as a newer, more dynamic nonhuman identity class because they can reason, plan and execute multi-step tasks with delegated authority.
- Authentication mechanism. Record how the subject proves legitimacy: static secret, signed token, certificate, hardware- or attestation-backed credential, or cloud-native temporary credential. These are mechanisms, not subject types. A record can note multiple mechanisms and their lifecycle—for example, a credential issued for a particular runtime or task, then expired or revoked.
- Autonomy and action. Classify the operating mode as recommendation only, action after approval, fixed-workflow action, or autonomous action within bounded policy. Separately list action types such as read, create, modify, send, approve, delete, transfer and execute. A “low-autonomy” label does not make a permission harmless if the agent can perform a sensitive or irreversible operation.
- Delegation and authority. Identify who or what delegated the task, its purpose, maximum scope, downstream recipients and any human approval. Record whether each downstream scope is narrower than its parent. NIST recommends cryptographically linking an agent to the initiating entity and attenuating authority as it moves to sub-agents or downstream services.
- Lifecycle and persistence. Mark the identity as persistent, ephemeral, task-created, environment-bound or tied to a workload deployment. Track issuance, rotation, expiry, revocation, last use, owner changes, suspension and decommissioning. CSA notes that workload identities can be highly ephemeral, while legacy service or integration accounts can become stale or orphaned.
- Data and environment risk. Record the environments, data sources and resources it can reach; distinguish development, test and production; and note crossings between organizational or trust boundaries. Include the prompt and data-input provenance needed for risk and policy decisions, without treating every prompt as a credential or a complete record of intent.
For example, an agent that reads support tickets and drafts a response for approval should not share a classification with an agent that can send messages, change customer records or issue refunds. The distinction is not the model behind either system; it is the authority, action path, review point, data access and delegation chain. Classification should make those differences visible to the people deciding access and oversight.
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Use the inventory as an input to enforceable policy, not as a descriptive catalog. NIST’s design tenets favor bounded execution and task-limited authorization; Microsoft Learn similarly cautions against letting the agent decide authorization for itself.
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- Give each agent a distinct, verifiable identity. Tie it to the approved workload, environment and configuration. NIST says unique, cryptographically verifiable identification helps prevent broad impersonation and improves traceability. Do not rely only on a shared service account or an agent-supplied name in a log.
- Grant the minimum authority for the task. Prefer permissions issued when work begins and scoped to that task. Avoid persistent access when the agent is idle, and separate read permission from write, approval, deletion or transfer rights.
- Use short-lived, revocable authorization. Where the architecture supports it, use time-bounded, audience-specific grants, runtime verification and revocation when behavior, context or risk changes. Avoid long-lived bearer tokens and static reusable secrets where a narrower, temporary mechanism is practical.
- Enforce decisions outside the model. The application, identity system and policy layer should evaluate whether a tool call or resource access is allowed. The model can reason about the next step, but deterministic controls must decide whether that step is permitted. Microsoft Learn states: “Do not let the agent decide on authorization by itself.”
- Make tool permissions narrow. A connector should not inherit more authority than the task requires. If an agent only needs to read a record, its tool identity should not also be able to modify or delete it. Keep tool and resource identities visible so an agent’s permission is not inferred from the connector’s name.
- Set meaningful human-review thresholds. Require review for sensitive or irreversible actions, with the action, target, authority and relevant context visible to the reviewer. Avoid approval prompts so frequent that people approve automatically; the point is to reserve human judgment for consequential thresholds.
- Log actions so they can be attributed. Capture the task originator, agent identity, delegated scope, tool, resource, action, relevant data context, policy decision, result and any human approval. Log downstream calls as connected events, not as unrelated activity attributed only to a generic service principal.
These controls are related but not interchangeable. A distinct identity improves attribution; it does not, by itself, limit permissions. Short-lived credentials reduce the window of exposure; they do not prove that an action was authorized. Human approval can add oversight; it does not replace deterministic authorization checks. The inventory should make it possible to see which control applies at each stage.
How do I audit an agent’s actions back to the person who authorized them?
Preserve attribution through delegation, including when an agent calls a tool or hands work to another agent. A useful audit chain links the initiating human or organization to the task and mandate, the agent identity, any downstream agent or tool identity, the target resource, the policy decision and the outcome.
For each task, record enough context to answer: who or what initiated it; what purpose and constraints were authorized; which agent acted; what scope it received; which tool and resource were involved; what action was attempted; whether policy allowed it; whether a person approved it; and what happened. Include relevant prompt and data-source provenance where it affects risk or explains the action. NIST and CSA both emphasize traceability across the chain, while NIST’s concept paper specifically calls for linking user identities to agents and logging actions and prompt or data-input provenance.
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Do not assume that recording a username at the beginning is sufficient. If a task fans out to several tools, downstream actions need correlation to the initiating task and the authority passed to each recipient. Delegated scopes should be verifiable and narrower than the parent mandate. If the chain breaks at a shared credential, the audit may show that a service acted but fail to establish which task or delegator authorized it.
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Design logs and operational procedures together. Define who can search them, how an incident responder can suspend an identity or revoke a grant, and how an approval or policy decision is correlated with the actual resource action. Keep retention and access to provenance appropriate to the sensitivity of the data; the inventory should state what evidence exists and where it is maintained, rather than imply that every system stores identical context.
Which standards and building blocks can support the inventory?
There is no single mature agent-identity standard that solves discovery, delegation, authorization and audit end to end. NIST’s NCCoE concept paper identifies technologies to evaluate as implementation building blocks, while describing its design tenets as a starting point and noting that current standards and technology will not immediately satisfy every tenet.
- OAuth 2.0 and extensions: Authorization building blocks for scoped access and grants.
- OpenID Connect: Authentication and identity claims.
- SPIFFE/SPIRE: Workload identity and attestation patterns.
- SCIM: Provisioning and deprovisioning support.
- Policy-based access controls: A place to enforce decisions independently of model-generated reasoning.
- MCP-related tool access patterns: A relevant area to evaluate when agents connect to tools through MCP.
These technologies address different parts of the system; choosing one does not automatically create a complete agent identity, bind every action to a human delegator or guarantee least privilege. Evaluate how the building blocks fit the existing identity and runtime architecture, what information they carry across delegation, and what operational gaps remain.
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- Define the inclusion boundary and ownership model. State which capabilities make a system an in-scope agent, how it differs from an ordinary model call, and which team owns the record and incident response.
- Discover across systems. Search directories, cloud and SaaS identity, application and workload records, code and CI/CD, tool registries, connectors, secrets systems and logs.
- Deduplicate without merging unlike identities. Link agent, workload, application, tool and resource records, while retaining separate identifiers and credential mechanisms.
- Assign stable identifiers and accountable owners. Resolve unknown owners and establish incident contacts before broadening deployment.
- Map authority and data flow. Record delegators, downstream recipients, tools, resources, data classifications, environment boundaries and current privileges.
- Classify risk-relevant behavior. Document autonomy, action types, persistence, authentication and approval thresholds, then use those classifications to shape policy.
- Reduce excessive or orphaned access. Remove shared or ownerless identities where possible; replace broad standing access with task-scoped grants and a documented revocation route.
- Connect logs and lifecycle controls. Establish attributable logging, review queues, suspension, revocation and decommissioning evidence. Review last-seen activity and ownership changes.
- Pilot on high-impact agents, then refine. Begin with systems that can change important records, disclose sensitive data or take irreversible action. Learn where the taxonomy or workflow misses real relationships before scaling.
Prioritization should follow consequence and exposure: broad resource access, production reach, sensitive data, autonomous execution and weak delegation traceability all justify earlier review. This is a practical sequencing choice, not a substitute for discovering lower-impact identities that may later acquire more authority.
What should I evaluate in an identity product or internal registry?
Whether the implementation is an internal registry, an IAM extension, an NHI-management platform or an agent-identity product, evaluate it against the same operating requirements. A label or dashboard is not enough if it cannot connect the actor to effective access and action history.
- Does it create a distinct identity for each agent, or merely tag a shared workload?
- Can it link an agent to the human or organizational delegator and preserve that relationship downstream?
- Can delegated authority be narrowed, time-bounded, verified and revoked?
- Are agent, tool and resource identities separately visible?
- Can it discover shadow agents and orphaned credentials across cloud, SaaS, DevOps and on-premises systems?
- Do logs capture intent, delegation, tool calls, relevant data provenance, policy decisions and outcomes?
- Does it support owner transfer, approval thresholds, incident response, suspension and decommissioning?
- Does it depend on a particular cloud or identity provider, and what happens outside that environment?
- Can the security team sustain the operational overhead of keeping identities, grants and ownership current?
Use a representative end-to-end task to assess coverage: identify the originator, follow an agent through a tool call to a resource, verify the delegated scope and policy decision, and determine whether the final action can be attributed and revoked or contained. This is an evaluation method, not a claim that any particular product has passed it.
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What common inventory mistakes should I avoid?
- Calling every model call an agent. Use capability and authority—tool selection, data access and consequential action—to define scope. Otherwise the register fills with low-value entries while actionable actors remain hard to distinguish.
- Treating the model as the identity. Models can be shared, replaced or served through different applications. Govern the actor, workload and access path rather than making a model name the sole security record.
- Using one privileged account for several agents or tools. A shared account blurs attribution and can give every connected task the same excessive reach. Separate identities and permissions where feasible.
- Recording a credential but not its subject. A token or certificate explains how a system authenticates, not which agent it represents or who authorized its work.
- Trusting the agent to police itself. The model may propose an action, but authorization belongs in application, identity and policy controls.
- Logging only the final API call. Without originator, mandate, delegation and policy context, a technically complete resource log may still fail to explain why the agent acted.
- Keeping broad standing privileges because the agent is “internal.” Internal placement does not make persistent access task-appropriate. Use least privilege and task-bounded grants where the architecture permits.
- Creating a registry without lifecycle ownership. Stale service accounts and ephemeral workloads require different handling. Define expiry, rotation, owner transfer, suspension and retirement evidence rather than assuming records remain accurate.
- Assuming one standard or product settles the question. The ecosystem is evolving. NIST presents its tenets as a starting point; validate the gaps among identity, authorization, delegation and audit components in your own architecture.
Frequently Asked Questions
Do AI agents need a separate identity from the service account that runs them?
Where an agent can exercise delegated authority, a distinct agent identity improves the ability to distinguish it from the workload or service account that hosts it. If the architecture cannot yet separate them, document the relationship and the attribution gap rather than presenting the shared account as a complete agent identity.
Should prompts be stored in an AI-agent audit log?
Record prompt and data-source provenance to the extent it is needed to explain risk decisions and actions, but do not assume every system should retain every prompt verbatim. Set retention and access controls according to data sensitivity and the investigation needs the organization must meet.
What is the difference between an AI agent identity and an API key?
An agent identity represents the actor whose authority and actions need governance; an API key is one possible credential mechanism. The key may be rotated, replaced or shared by a poorly designed integration without changing which actor the inventory is meant to describe.
Can a human approval step replace access controls for an AI agent?
No. A reviewer can provide oversight for a sensitive action, but application and policy controls still need to determine whether the agent is authorized to attempt it. Approval should be tied to the task and action, and not become a blanket substitute for least privilege.
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