Topic starter
30/09/2026 7:10 pm
TL;DR: P0 Security says AI agents are now acting across more identities, tools, and sensitive systems than traditional access controls were designed to govern, and argues that security teams need runtime policy, full action-chain visibility, and just-in-time access to preserve auditability and limit privilege sprawl.
Editorial analysis by NHI Mgmt Group, based on content published by P0 Security: “IDAC podcast: How to flip from traditional PAM to JIT access”.
Key questions
Q: How should teams govern runtime access for AI agents and NHIs?
A: Teams should treat runtime access as a live authorisation problem, not just a provisioning problem.
Q: Why does standing privilege create more risk for agents than for humans?
A: Standing privilege is riskier for agents because their actions can unfold faster, across more tools, and with less predictable timing than a human workflow.
Q: What breaks when security teams cannot see the full action chain?
A: Without the full action chain, teams can see that access existed but not how it was used across tools, systems, and decisions.
Practitioner guidance
- Implement runtime access enforcement Require access decisions to be evaluated at the point of use for AI agents, NHIs, and human operators that touch sensitive systems.
- Map the full action chain Record which identities, tools, and policy checks were involved in each step of a task so investigators can reconstruct what happened after the fact.
- Reduce standing privilege exposure Review where always-on access still exists across developer, on-call, machine, and agent workflows, then narrow those paths to just-in-time access wherever operationally possible.
Bottom line: AI agents and NHIs challenge access models that assume privilege can be granted once and safely left in place.
What's in the full article
P0 Security's full resource covers the operational detail this post intentionally leaves for the source:
- How the runtime access platform enforces policy at the moment an AI agent or NHI requests access
- Examples of how privilege discovery maps access across users, machines, and agents in one control plane
- What a complete audit trail looks like when the access decision and the resulting action need to be proven together
- How the vendor positions just-in-time access for developer workflows, on-call use, and breakglass scenarios
👉 Read P0 Security's analysis of runtime access control for AI agents and NHIs →
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30/09/2026 7:11 pm
Runtime access is becoming the governing layer for non-human and agentic identity. The article reflects a shift that many IAM programmes have not yet internalised: access is no longer only an entitlement state, it is an execution-time decision. For AI agents and NHIs, that is the point at which risk must be measured and constrained. Organisations that keep treating access as a static property will continue to miss the moment when privilege is actually used.
A few things that frame the scale:
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How do organisations decide between just-in-time access and always-on access?
A: Use just-in-time access when the action is sensitive, time-bound, or reversible only with difficulty. Always-on access should be reserved for the narrowest set of operational needs, because any persistent privilege increases the chance that an AI agent, NHI, or human workflow will drift beyond its intended scope.
👉 Read our full editorial: Runtime access for AI agents and NHIs needs just-in-time control