Topic starter
25/06/2026 12:49 am
TL;DR: P0 Security finds that agentic AI applications are managing cloud resources, secrets, and builds through shared tokens, long-lived credentials, and hardcoded service accounts that remain outside standard IAM reviews. Governance must shift from reviewing stable access to governing dynamic machine actors whose effective privilege changes across runtime context.
Editorial analysis by NHI Mgmt Group, based on content published by P0 Security: “Ten things to understand when using agentic AI applications”.
Key questions
Q: What breaks when AI systems are governed like static applications?
A: Lifecycle drift breaks the model.
Q: Why do shared service accounts create risk for AI agents?
A: Shared service accounts hide which agent made a request, so they collapse accountability and make incident response slower.
Q: How do IAM teams measure whether AI agent access is under control?
A: IAM teams should measure whether they can describe every agent action path, every context source, and every parallel execution pattern in audit terms.
Practitioner guidance
- Audit agent credentials as first-class NHIs Create a complete inventory of shared tokens, hardcoded credentials, service accounts, and API keys used by agentic systems.
- Separate agent authority by task and environment Prevent a single agent identity from carrying the same access across development, test, and production.
- Move reviews to effective access paths Review the actions an agent can actually reach through chained APIs, build systems, and secret stores, not just the role name attached to the credential.
Bottom line: Agentic AI systems create an NHI governance problem because they act with delegated authority while still relying on shared tokens, long-lived credentials, and hardcoded service accounts.
What's in the full article
P0 Security's full analysis covers the operational detail this post intentionally leaves for the source:
- Examples of how agentic software uses cloud, build, and secret-management access in practice
- The article's discussion of runtime autonomy versus traditional automation in identity terms
- Guidance on why access review, remediation, and lifecycle controls must change for machine actors
- The source's treatment of context-aware enforcement, just-in-time access, and policy-based governance
👉 Read P0 Security's analysis of agentic AI identity governance and NHI blind spots →
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25/06/2026 9:51 am
Agentic AI identity governance exposes an NHI control gap, not a new category of user. These systems are still non-human identities in operational terms, but they act with enough runtime variability that static IAM assumptions no longer hold cleanly. The practical lesson is that the governance model must follow the actor's behaviour, not the interface label.
A few things that frame the scale:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: When should organisations treat an AI system as a non-human identity?
A: Treat an AI system as an NHI when it can authenticate, request tools, or perform actions without direct human supervision. At that point it needs inventory, lifecycle, least privilege, monitoring, and revocation controls just like other machine identities.
👉 Read our full editorial: Agentic AI identity governance is exposing new NHI blind spots