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14/09/2026 10:10 am
TL;DR: AI agents cannot be governed reliably through a universal identity directory because they are ephemeral, non-enumerable, and runtime-issued, according to Akeyless. The article’s deeper point is that static RBAC and ABAC fail when the actor can change actions mid-session, making intent-aware enforcement the new control boundary.
NHIMG editorial — based on content published by Akeyless: Stop Trying to Build a Directory for Ghosts: Why the IdP-Centric Model for AI Agent Identity Is Already Failing and What to Do Instead
By the numbers:
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
- Only 5.7% of organisations have full visibility into their service accounts.
Questions worth separating out
Q: What breaks when AI agents are managed like ordinary machine identities?
A: What breaks is the assumption that access scope can be fully understood from provisioning data and quarterly review.
Q: Why do traditional RBAC and ABAC controls fall short for AI agents?
A: They usually assume one stable identity making one request at a time.
Q: How can security teams govern AI agents without a directory for every agent instance?
A: They should govern the runtime, not the instance.
Practitioner guidance
- Anchor agent access in runtime attestation Use cloud IAM, Kubernetes service accounts, OIDC federation, or SPIFFE/SPIRE as the identity source of record for agent execution.
- Bind authorisation to Auth Methods and target systems Define policy around which attested runtimes may reach which systems, rather than assigning long-lived scopes to named agent identities.
- Move sensitive actions behind a gateway Require every agent action against enterprise systems to traverse an enforcement point that can inspect intent, prompt context, target object, and session history before issuing credentials.
What's in the full article
Akeyless' full post covers the operational detail this post intentionally leaves for the source:
- Specific examples of how Auth Methods are defined for AWS, Kubernetes, OIDC, and SPIFFE-backed workloads
- The gateway enforcement model for inspecting agent actions against declared intent before credentials are issued
- The control flow for short-lived credential brokering and session-level containment
- The architecture comparison between synthetic agent directories and workload-anchored identity governance
👉 Read Akeyless' analysis of AI agent identity and workload enforcement →
AI agent identity risk is a workload problem, not an IdP problem?
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14/09/2026 10:11 am
AI agent identity is collapsing the directory-first assumption that IAM inherited from human users. Directory models assume the identity exists long enough to be enrolled, assigned, and reviewed. That assumption fails when an agent can be born, act, and vanish inside a single runtime window. The implication is that identity governance must stop treating enumeration as the starting point for protection.
A few things that frame the scale:
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
- 71% of NHIs are not rotated within recommended time frames, increasing the risk of compromise over time.
A question worth separating out:
Q: Should organisations treat AI as an application or as an identity?
A: Treat it as an identity when the AI can access data, invoke tools, or participate in workflows that affect business systems. That framing makes least privilege, just-in-time access, and lifecycle governance relevant. If you keep treating it only as an application, you will miss the access and delegation behaviours that actually create risk.
👉 Read our full editorial: AI agent identity needs workload enforcement, not a ghost directory
