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25/06/2026 1:11 am
TL;DR: AI agents are runtime decision-makers that can initiate workflows, call APIs, and act on changing inputs, while NHIs are machine credentials built to authenticate systems in predictable ways, according to Silverfort. Treating them as interchangeable obscures different governance, monitoring, and lifecycle controls, and leaves both privilege and accountability gaps exposed.
NHIMG editorial — based on content published by Silverfort: AI agents and non-human identities are different security problems
By the numbers:
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams govern AI agents that use existing machine credentials?
A: Treat the credential and the decision-maker as separate governance objects.
Q: Why do AI agents complicate least-privilege design?
A: Because least privilege assumes the actor's intent is known when access is granted.
Q: What breaks when teams treat AI agents like ordinary NHIs?
A: Teams often secure the credential but ignore the runtime behaviour.
Practitioner guidance
- Separate credential governance from agent governance Map which access paths belong to service accounts, API keys, and workload identities, then identify where an AI agent can act through them.
- Constrain agent actions, not just agent access Define explicit tool, data, and workflow limits for each agentic system, then verify that the limits hold at runtime.
- Review lifecycle offboarding for delegated AI authority When a system, workflow, or business objective changes, revoke the machine credentials and remove the delegated authority attached to the agent.
What's in the full article
Silverfort's full blog post covers the operational distinctions this analysis intentionally leaves at framework level:
- The article includes a side-by-side breakdown of AI agents and NHIs for engineering and architecture teams.
- It expands on how lifecycle, visibility, and oversight differ between static machine identities and autonomous systems.
- It shows where access control, monitoring, and accountability split once an AI system begins to act on its own.
👉 Read Silverfort's analysis of AI agents vs NHIs and identity governance →
AI agents vs NHIs: where identity teams keep mixing them up?
Explore further
25/06/2026 10:27 am
AI agent identity risk is a different class of governance problem than NHI security. NHIs are designed to be predictable credentials, but AI agents can decide what to do with those credentials at runtime. That means the same access token can produce very different risk outcomes depending on whether the actor is a static workload or a decision-making system. Practitioners should stop treating agentic behaviour as a minor extension of machine identity.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: How do IAM teams decide whether an AI agent needs new controls?
A: They should ask whether the system can make independent decisions that change the sequence, timing, or selection of actions. If it can, standard NHI controls are incomplete and the programme needs behavioural oversight, explicit task scope, and offboarding rules for delegated authority.
👉 Read our full editorial: AI agents and NHIs are different identity problems
