What is the difference between managing AI agents as users and as NHIs?

Managing agents as users assumes stable roles, human oversight, and a normal offboarding process. Managing them as NHIs focuses on machine authentication, credential lifecycle, and automated revocation. For autonomous systems, the second model is more accurate because the security risk is the identity itself, not a human account standing behind it.

Why This Matters for Security Teams

Managing AI agents as users sounds familiar because it fits existing IAM, RBAC, and offboarding workflows. The problem is that autonomous agents do not behave like people. They can chain tools, act on goals, and change their access needs mid-task. That makes a normal employee lifecycle a poor fit for the real risk surface: the machine identity, its secrets, and the permissions it can exercise at runtime.

This is why guidance is shifting toward treating agents as NHIs first, then layering intent-based authorisation on top. Current practice is to combine workload identity, short-lived credentials, and policy checks that evaluate the request context rather than a static job title. For background on the broader NHI lifecycle, see Ultimate Guide to NHIs and the deeper lifecycle discussion in Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs. The industry still has gaps: NHI Mgmt Group notes that only 20% of organisations have formal offboarding and API key revocation processes, which is a serious mismatch for autonomous systems. In practice, many security teams discover that mismatch only after an agent has already accessed data or executed an action outside its intended scope.

How It Works in Practice

The operational difference is simple: user management assumes a stable subject with a predictable role, while NHI management assumes a workload that needs machine authentication, ephemeral secrets, and tight revocation. For agents, that means issuing identity to the workload itself, not to a pretend human persona. A common pattern is workload identity backed by cryptographic proof, then JIT credential provisioning per task so the agent receives access only for the minimum time required.

That model is stronger because it fits the way agents actually operate. They may call APIs, retrieve context, pass through multiple tools, or request new privileges as the task evolves. Static RBAC cannot keep pace with that behaviour. Instead, intent-based or context-aware authorisation evaluates whether the requested action matches the declared goal, the current state, the data sensitivity, and the execution environment. Standards and guidance such as the OWASP Agentic AI Top 10 and NIST AI Risk Management Framework both support the idea that governance must be evaluated at decision time, not just at onboarding.

Practically, teams should:

• Bind each agent to a distinct workload identity rather than a shared service account.
• Use short-lived tokens and revoke them automatically when the task completes.
• Store secrets in a manager and rotate them aggressively instead of embedding them in code or config.
• Log agent actions separately from human actions so audit trails remain defensible.
• Gate high-risk actions with policy-as-code and real-time approval controls.

This guidance tends to break down in long-running, multi-tool agent pipelines because privilege boundaries blur when one agent hands work to another without a clean task termination point.

Common Variations and Edge Cases

Tighter agent controls often increase operational overhead, so organisations have to balance security against execution speed and developer friction. That tradeoff is real, especially when the agent is embedded in CI/CD, support automation, or data analysis workflows where frequent re-authentication can disrupt productivity. Best practice is evolving, and there is no universal standard for every agent pattern yet.

One common edge case is the shared platform agent. If multiple users trigger the same backend agent, teams sometimes try to reuse one service identity for simplicity. That can work for low-risk automation, but it weakens attribution and makes revocation messy. Another edge case is delegated access, where an agent acts on behalf of a user. In that model, identity needs to preserve both the user context and the agent context so policy can distinguish who authorised the action and what the agent actually did. For more on the attack surface created by agent behaviour, see OWASP NHI Top 10 and AI Agents: The New Attack Surface report.

A useful rule of thumb is to ask whether the subject is stable enough to manage like a user. If the answer is no because the system is goal-driven, adaptive, or able to chain tools, then the safer model is NHI management with intent-based checks, ephemeral credentials, and Zero Trust controls. NHI risk is especially sharp when secrets are long-lived, because autonomous behaviour can widen blast radius faster than a human can notice. That is why current guidance from CSA MAESTRO agentic AI threat modeling framework and NIST Cybersecurity Framework 2.0 emphasizes continuous control validation rather than one-time provisioning.

Related resources from NHI Mgmt Group

• What is the difference between managed identities and hardcoded secrets for AI agents?
• What is the difference between workload identity and API keys for AI agents?
• What is the difference between logging actions and logging intent for AI agents?
• What is the difference between human identity governance and AI agent governance?