Should organisations treat autonomous agents like human users or service accounts?

Organisations should not treat autonomous agents as simple human analogues. They behave like governed non-human identities with added runtime decision-making, so they need identity boundaries, action checkpoints, and clear accountability. Human-style certification cycles alone are too slow for systems that can complete sensitive work within one session.

Why This Matters for Security Teams

Autonomous agents sit between a human user and a service account: they are software workloads with identity, but they also make runtime decisions, chain tools, and act on goals. That means the usual binary choice is too blunt. Human accounts assume a person approves each step; service accounts assume predictable machine-to-machine behaviour. Agents need both identity governance and execution guardrails, especially when they can touch production systems, customer data, or secrets. Current guidance suggests treating them as governed NHIs with explicit authority boundaries, not as “smart users” with broad standing access. The risk is not theoretical: SailPoint reports that 80% of organisations say their AI agents have already acted beyond intended scope, and NHI risk is amplified when access is not visible or short-lived, as covered in the OWASP NHI Top 10 and the OWASP Agentic AI Top 10. In practice, many security teams encounter agent overreach only after credentials have been reused, data has been exposed, or an automated workflow has already completed the wrong action.

How It Works in Practice

The practical model is closer to zero standing privilege for a workload than to traditional user lifecycle management. An agent should have a dedicated workload identity, short-lived credentials, and a policy layer that evaluates intent at request time. That usually means issuing JIT credentials per task, scoping them to the smallest viable action set, and revoking them automatically on completion. Where possible, teams should prefer workload identity primitives such as SPIFFE/SPIRE or OIDC-backed service identities so the platform can verify what the agent is, while policy determines what it may do next. NIST’s NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework both support this shift toward continuous governance rather than one-time approval. For NHI operations, the same logic applies to secrets: keep them ephemeral, never embed them in code, and avoid standing tokens that outlive the task. The NHI guidance is consistent with broader research, including the Ultimate Guide to NHIs — What are Non-Human Identities, which shows how weak rotation and poor visibility turn machine identities into durable attack paths. A useful operating pattern is: authenticate the agent, evaluate the request in context, allow one bounded action, log the result, then expire the privilege immediately. These controls tend to break down when agents are allowed to orchestrate other agents through shared credentials, because the boundary between “one task” and “many tasks” disappears.

• Use a dedicated workload identity for each agent or agent role.
• Issue short-lived credentials only when a task is approved.
• Enforce intent-based authorisation with policy-as-code at request time.
• Restrict tool use, data access, and network reach per action.
• Revoke secrets automatically and log every privileged step.

Common Variations and Edge Cases

Tighter control often increases orchestration overhead, requiring organisations to balance speed against assurance. That tradeoff is real, especially for multi-agent systems, autonomous coding tools, or agents that operate across SaaS, CI/CD, and internal APIs. There is no universal standard for this yet, but best practice is evolving toward context-aware authorisation rather than static RBAC alone. A human-style access review might still matter for ownership and accountability, yet it cannot safely substitute for runtime checks when an agent can complete a sensitive workflow in seconds. Another edge case is delegated execution: if an agent is acting on behalf of a person, the person’s approval should not automatically inherit all the person’s entitlements. Instead, the agent should receive a narrower, time-boxed delegation token with explicit scope. This is also where breach patterns matter: NHIMG’s AI LLM hijack breach and Moltbook AI agent keys breach show how quickly exposed agent credentials can become active abuse paths. The right question is not “is this a user or a service account?” but “what identity, authority, and revocation model fits this autonomous workload right now?” For many deployments, the answer is a governed NHI pattern with agentic guardrails, not a permanent human analogue.

Related resources from NHI Mgmt Group

• How can organisations govern AI agents that use service accounts and tokens?
• What breaks when organisations manage service accounts like human users?
• Should organisations treat service accounts like user accounts in Dynamics controls?
• Why do service accounts and AI agents need different controls from human users?