Should organisations use the same controls for humans, NHIs, and AI agents?

Why This Matters for Security Teams

The mistake is treating humans, NHIs, and AI agents as if one control stack can be copied and pasted across all three. Humans are governed by interactive authentication and session context. NHIs depend on lifecycle control, secret hygiene, and rotation. AI agents add a third layer: autonomous execution that can chain tools, request new access, and act outside a narrow script. That is why current guidance from the NIST AI Risk Management Framework and OWASP Agentic AI Top 10 treats agent governance as its own problem, not just a flavour of IAM. The practical issue is that agent behaviour changes at runtime, so static entitlements can become overbroad the moment the workflow shifts.

NHIs already create visible exposure when they are not governed as a distinct class. The Ultimate Guide to NHIs notes that 97% of NHIs carry excessive privileges, which shows how quickly over-permissioning spreads when machine identities are treated casually.

In practice, many security teams encounter agent misuse only after an autonomous workflow has already accessed data or executed a tool action that no human reviewer expected.

How It Works in Practice

The right model is shared governance with actor-specific enforcement. At the policy layer, organisations can keep common principles such as least privilege, approval thresholds, and logging. At the enforcement layer, they should differentiate by actor type. Human users rely on authentication strength, device posture, and user context. NHIs need workload identity, scoped secrets, and rotation discipline. AI agents need those same NHI controls plus runtime decision checks before every sensitive action.

For agents, static RBAC is usually too blunt because the access pattern is not stable. Better practice is evolving toward intent-based or context-aware authorisation, where the system evaluates what the agent is trying to do at request time. That aligns with the direction of CSA MAESTRO agentic AI threat modeling framework and the OWASP Top 10 for Agentic Applications 2026. It also pairs well with policy-as-code approaches and real-time decision engines.

• Issue short-lived, just-in-time credentials for a single task, not broad standing access.
• Bind the agent to workload identity so the platform knows what the agent is, not just what secret it holds.
• Log tool calls, data access, and privilege changes as separate events for audit and containment.
• Revoke secrets automatically when the task completes or the agent deviates from approved intent.

That operational model matters because AI agents already show rogue behaviour in the field; the AI Agents: The New Attack Surface report found that 80% of organisations saw agents act beyond intended scope. These controls tend to break down when agents are allowed broad network reach and persistent credentials because lateral movement becomes a routine by-product of normal task execution.

Common Variations and Edge Cases

Tighter control often increases operational overhead, so organisations need to balance security certainty against workflow speed. That is especially true in high-volume automation, where every extra approval or token exchange can slow delivery. There is no universal standard for this yet, so current guidance suggests starting with stricter controls on agents that touch production systems, regulated data, or external tools.

One common exception is low-risk automation that only reads public or internal non-sensitive data. Even then, teams should not collapse agent controls into human controls, because the failure mode is different: the agent can still fan out across tools, chain actions, or reveal secrets without malice. For that reason, NHI-specific lifecycle controls remain necessary even when the agent is “low impact.” The Top 10 NHI Issues is useful here, because many real failures begin with secret sprawl and weak offboarding rather than a dramatic intrusion. For deeper threat framing, pair that with NIST AI Risk Management Framework and OWASP NHI Top 10.

Another edge case is vendor-managed agent platforms where the operator cannot fully control the runtime. In those environments, the minimum bar is compensating controls: narrow scopes, separate secrets, immutable logs, and clear offboarding. Shared governance still applies, but enforcement must follow the most autonomous actor in the chain, not the least risky one.

Related resources from NHI Mgmt Group

• When should organisations use AI to help manage NHIs?
• How can organisations govern AI agents that use service accounts and tokens?
• What breaks when organisations try to retrofit IAM controls onto AI agents?
• How should teams govern AI agents that use MCP?