Notifications
Clear all
Topic starter
25/06/2026 12:49 am
TL;DR: Agentic AI systems are acting on cloud resources, secrets, and builds with shared tokens, hardcoded credentials, and long-lived service accounts, making unmanaged NHIs a growing governance blind spot, according to P0 Security. Access review cycles assume stable, reviewable privilege, but autonomous actors can plan and execute faster than those controls can observe.
NHIMG editorial — based on content published by P0 Security: Ten things to understand when using agentic AI applications by Shashwat Sehgal
Questions worth separating out
Q: How should security teams govern access for agentic AI systems?
A: Security teams should govern agentic AI systems as privileged non-human identities with unique ownership, explicit purpose, and a documented revocation path.
Q: Why do agentic AI systems complicate least privilege programmes?
A: They complicate least privilege because the risky part is not only the initial entitlement, but the sequence of actions the agent can choose at runtime.
Q: What breaks when AI agents rely on shared tokens and service accounts?
A: Ownership, auditability, and offboarding all break when multiple agents or workflows share the same credentials.
Practitioner guidance
- Map every agent to a unique identity record Create a dedicated inventory for AI agents, service principals, tokens, and secrets so each actor has one owner, one purpose, and one revocation path.
- Rebuild access reviews around effective blast radius Assess which cloud resources, build systems, and secret stores an agent can actually reach, not just which role it has on paper.
- Enforce runtime context for privileged agent actions Gate sensitive actions on environment, source, and request type, then require stronger controls when an agent crosses from low-risk to high-risk workflows.
What's in the full article
P0 Security's full analysis covers the operational detail this post intentionally leaves for the source:
- How the article maps agentic AI behaviour to shared tokens, hardcoded secrets, and long-lived service accounts in real environments.
- Which identity and governance questions should be asked before operationalising copilots, bots, or infrastructure automation agents.
- The article's discussion of continuous context, just-in-time elevation, and access review as machine-identity controls.
- The source's framing of how agent output becomes an execution path when change control is bypassed.
👉 Read P0 Security's analysis of ten identity and governance risks in agentic AI →
Agentic AI identity governance: what IAM teams need to know?
Explore further
View Full Forum → | NHI Foundation Course → | Our Services →
25/06/2026 9:51 am
Agentic AI governance is an identity problem before it is an AI problem. The article describes software actors that can execute real-world actions across cloud and application systems, which makes their authority the primary security question. Once a system can plan and execute dynamically, identity, privilege, and auditability become inseparable. Practitioners should stop treating these systems as peripheral automation and start governing them as privileged actors.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant behaviour gap, according to The State of Secrets in AppSec.
A question worth separating out:
Q: Who is accountable when an AI agent changes cloud resources or secrets?
A: Accountability should sit with the business owner of the agent, the identity team that issued the access, and the control owner that approved the action path. If no one can name those three roles, the governance model is incomplete. For regulated or high-risk environments, that missing accountability becomes an operational and audit problem.
👉 Read our full editorial: Agentic AI identity governance is exposing new NHI blind spots
