What is an AI agent identity?

By Identra · Updated October 3, 2026

An AI agent identity is the identity used to authenticate an AI agent and associate its actions with permissions and an accountable owner. It lets systems distinguish the agent doing the work from any user or service whose authority it exercises.

Key numbers

Machine identities outnumber human identities 82 to 1 in enterprises, before agent adoption peaks (CyberArk Identity Security Landscape, 2025)
91% of organizations already use AI agents, but only 10% have a well-developed strategy for managing non-human identities (Okta AI at Work, 2025)
40% of enterprise applications will feature task-specific AI agents by end of 2026, up from under 5% in 2025 (Gartner press release, August 2025)

How does AI agent identity work?

An agent runs as software, presents credentials, and requests access to tools or resources. The receiving system authenticates the caller and decides whether the requested action is allowed. The identity identifies the principal. A credential proves access to that identity, while authorization determines what it may do.

A useful identity record connects the agent to its purpose, owner, runtime, and approved access. Execution records should also identify the particular task or run. These details need not all live inside a token, but security teams should be able to connect them during an investigation.

An agent may act under delegated user authority or under permissions assigned directly to an application. Scheduled agents do not always have a human initiating each run. Both models need an accountable owner and explicit access boundaries.

How is an agent identity different from a service account?

An AI agent identity is a type of non-human identity. A service account can provide its underlying identity. The important distinction is how that identity is governed and attributed, rather than whether it uses a special credential format.

Traditional software can behave dynamically, and agents can follow tightly constrained workflows. Neither account type guarantees predictable behavior. Agents that select tools from model output make it especially important to separate permission to use a tool from approval of a particular action.

Agent identity
What it represents
The agent principal
Security question
Which agent requested this action?
Service account
What it represents
An account used by software
Security question
Which workloads share its access?
Credential
What it represents
Evidence used to authenticate
Security question
Who can use it, and when does it expire?
Delegation
What it represents
Authority granted to another actor
Security question
Whose authority applies, and within what limits?

Concept	What it represents	Security question
Agent identity	The agent principal	Which agent requested this action?
Service account	An account used by software	Which workloads share its access?
Credential	Evidence used to authenticate	Who can use it, and when does it expire?
Delegation	Authority granted to another actor	Whose authority applies, and within what limits?

How should agents act on a user's behalf?

AI agent delegation should preserve the distinction between the agent taking an action and the user or service granting authority. Copying a user's session into an agent can obscure that distinction and expose access beyond the task.

Use supported delegation mechanisms that constrain the target service and permitted operations. Token formats and provider support vary. Do not assume that a token automatically preserves every actor in a chain or restricts access to the current task.

When an agent calls a connector or another agent, check which identity reaches the destination. If a connector uses its own account, enforce caller-specific restrictions before it exercises that account's privileges. Correlate the initiating request with the resulting action, even where the destination cannot record the full delegation context.

What does an agent identity failure look like?

Consider a hypothetical support agent that reads tickets and drafts replies. It shares a CRM service account with a reporting application. That account can export customer records, although the support workflow only needs access to the customer associated with the current ticket.

A malicious ticket contains a prompt injection asking the agent to retrieve unrelated customer data and include it in a reply. If the agent follows the instruction and the tool permits the request, a valid credential can authorize harmful activity. Authentication alone does not establish that an action serves the approved task.

A dedicated identity makes attribution and containment easier, but does not by itself prevent this attack. The tool should restrict customer access to the authorized case, deny bulk export, and keep sending separate from drafting. Shared credentials also complicate response because disabling them can interrupt unrelated reporting work.

How do you secure AI agent identities?

Apply least privilege at the resource and action level. A rule allowing CRM access is too broad when the task only needs to read a case. Enforce authorization outside the model's instructions. This aligns with OWASP's agent security guidance, which recommends independent checks of scope, privilege, and approval before execution.

Register each production agent with an owner, purpose, approved tools, and review date. Separate identities where agents need independent access or revocation.
Prefer short-lived credentials where supported. Restrict their audience and permissions. Store unavoidable long-lived secrets securely and rotate them.
Validate the caller and requested resource at tool boundaries. Deny requests outside the task's authorized scope, even when the credential is valid.
Require approval for consequential actions such as changing permissions or sending sensitive records. Bind approval to the exact action and target.
Record the agent, initiating user or service, task, tool, target, authorization decision, and outcome. Protect logs and avoid recording secrets.
Review access when tools, owners, or purposes change. Remove obsolete grants and disable abandoned agents.

How can you verify accountability and containment?

Start with a permitted workflow and verify that the AI audit trail connects the request to the agent and the destination action. Then attempt an unrelated resource read, an unapproved write, and a request using an expired credential. Confirm that enforcement rejects them and records a useful reason.

Rehearse stopping a compromised agent. Disable new runs, revoke supported grants and sessions, and address credentials already issued. Some tokens remain usable until expiration unless the receiving system supports earlier invalidation. A disabled registry entry alone may leave working access behind.

Finally, test the non-human identity lifecycle: retire the agent, remove connector access, and confirm that scheduled jobs cannot restart it with surviving credentials. Good identity design makes ownership, permitted actions, and containment verifiable.

How Identra thinks about it

Identra discovers AI agents across Microsoft 365 Copilot and Foundry, Google Workspace and Anthropic, with each agent's owner, and brings agent and other non-human identities into one view. Connected OAuth grants show the app, permissions and grantor, and analysts can revoke risky grants. On macOS and Windows endpoints, every AI agent run is recorded with the user, device, AI client and outcome.

Go deeper: AI security, built on identity

Frequently asked questions

Does every agent run need a separate identity?

Not necessarily. A stable agent identity can serve multiple runs, with a distinct run identifier for attribution. Separate identities are useful when tasks require different permissions, owners, or containment boundaries.

Can an AI agent use a service account?

Yes. Give it only the access its workflow needs and avoid sharing the account with unrelated applications. Keep enough execution context to attribute actions to the agent and task.

Does an AI model itself have an agent identity?

The identity usually belongs to the application or runtime acting as the agent. Different agents can use the same model while having different owners, permissions, and credentials.

Does agent identity prevent prompt injection?

No. Identity controls can limit what a manipulated agent can access and make its actions traceable. Preventing harmful execution also requires tool authorization, data boundaries, and appropriate approval checks.

Is an agent owner the same as a delegating user?

No. The owner is accountable for managing the agent. A delegating user grants authority for a task, and that user may differ from the owner.

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