Should enterprises use the same identity for an AI agent and its human user?

Why the human and the agent should not be collapsed into one account

The core issue is accountability, not convenience. If the human and the AI agent share one identity, you lose a clean boundary between who requested the work and what the agent was allowed to do. A distinct agent identity preserves attribution, lets policy differ by actor, and keeps revocation or containment from affecting the human account.

That separation also prevents the common mistake of granting the agent whatever the user can do by default. The safer model is to keep the human as the initiating principal and give the agent only the permissions it needs for the specific task, for the shortest practical time, with explicit bounds on tool use and data access.

How linked identities work in practice

A useful pattern is to treat the human and agent as two identities in one execution context. The human authenticates and authorises the task, then the agent receives a task-scoped identity or delegated token that reflects that approval. When the task ends, the agent identity or its grants can be expired, rotated, or revoked without changing the human’s standing access.

This model works best when the system records the relationship between the two identities, such as a task ID, approval record, or session correlation. That linkage is what makes later review possible: you can answer who started the action, which agent executed it, what policy applied, and which downstream systems were touched.

Agentic AI Identity Guide is the clearest reference point for the identity model behind delegation, registration, authentication and retirement. AI Agent Authorisation Guide reinforces the operational pattern of task-scoped access, per-action decisions and human approval where needed.

What breaks when you reuse the same identity

Identity reuse creates a larger blast radius than many teams expect. If the agent is compromised, misbehaves, or is tricked into a bad action, the attacker or failure path inherits the full authority of the human account rather than a narrowed agent permission set. That makes investigation harder and increases the chance that a temporary agent issue becomes a broader account-level incident.

It also blurs lifecycle decisions. Human accounts are usually governed for employment, access review and personal use, while agent identities need different expiry, ownership and offboarding logic. When both are merged, revocation becomes blunt: either the agent keeps too much standing privilege or the human account becomes tied to a fragile automation workflow.

Zero Trust for AI Agents is relevant because it frames the control objective as verifying the principal and the request, then removing standing privilege. For the same reason, AI Agent Observability, Audit and Incident Response Guide matters when you need to attribute actions and revoke agent access cleanly after an exception.

Risk and Threat Considerations

Reusing one identity for both roles concentrates trust and makes abuse easier to hide. A malicious prompt, a compromised tool, or a mistaken action can inherit the user’s permissions and look like normal user activity unless the agent is separately identifiable and separately governed.

Failure mechanism: The agent acts through the human account, so the environment cannot distinguish delegated automation from direct user action. That erases policy separation, weakens auditability, and makes targeted revocation or containment much harder.

Impact: A single compromise can expose broader data, trigger unauthorised changes, and force disruptive user-account resets instead of a narrower agent shutdown.

Practitioner Guidance

What to verify: Confirm that the agent has its own identity, its own audit trail, and its own revocation path, even when it operates on behalf of a human. If your logs cannot separately answer “who initiated” and “what executed,” the design is not yet safe enough for production use.

Decision rule: If the agent can act, write, approve, send, or delete on an enterprise system, do not share the user’s primary identity. Give the agent a narrower delegated identity or scoped token, and require a separate policy decision for privileged actions.

Practitioner takeaway: The real control goal is not to make the agent “trusted,” but to make its trust bounded, attributable, and revocable without collapsing the human’s identity with the machine’s authority.

Related resources from NHI Mgmt Group

• What is the difference between human identity governance and AI agent governance?
• Why do AI agents make non-human identity governance harder?
• Why do AI agents create new risk in non-human identity management?
• Why do AI agents increase non-human identity risk in existing IAM programmes?