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06/06/2026 11:21 am
TL;DR: A 2025 survey of 260 executives found 91% of organisations already using AI agents in production, but only 10% have a strategy for managing them as identities, according to Aembit. The gap is not just operational; access review processes assume stable, reviewable privilege, while agents can act, delegate, and compound risk at runtime.
NHIMG editorial — based on content published by Aembit: AI agent identity security and runtime access control
By the numbers:
- 91% of organizations are already using AI agents in production.
- Only 10% have a strategy for managing those agents as identities.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
Questions worth separating out
Q: How should security teams govern AI agents as identities?
A: Security teams should govern AI agents as distinct identities with explicit ownership, scoped delegation, runtime policy checks, and retirement controls.
Q: Why do AI agents break traditional IAM assumptions?
A: AI agents break traditional IAM assumptions because they do not follow fixed workflows.
Q: What do security teams get wrong about AI agent credentials?
A: Teams often treat AI agent credentials like ordinary service account secrets, then leave them long-lived and reusable.
Practitioner guidance
- Inventory every live AI agent and its delegated authority Create a register of agents, owners, upstream delegators, downstream tools, and the data sources each agent can reach.
- Replace long-lived secrets with task-scoped credentials Move high-risk agents to short-lived access with explicit scope boundaries, especially where the agent touches production data, regulated workloads, or infrastructure controls.
- Add runtime policy checks after authentication Evaluate posture, request context, resource sensitivity, and approved purpose at the moment of each agent action.
What's in the full article
Aembit's full article covers the operational detail this post intentionally leaves for the source:
- The step-by-step model for attestation-based authentication in AI agent environments.
- The credential design patterns for just-in-time access and scoped delegation across multi-step agent workflows.
- The monitoring model for agent-level audit trails, including delegation chain visibility and behavioural anomaly detection.
- The practical guidance for replacing static credentials in mixed legacy and federated environments.
👉 Read Aembit's analysis of AI agent identity security and runtime access control →
AI agent identity risk: what IAM teams need to change now?
Explore further
06/06/2026 12:07 pm
AI agent identity is not workload identity with a new label: The problem is not that agents authenticate differently, it is that they decide differently. Traditional NHI controls assume a predefined action surface, while agents can choose tools, sequence actions, and change scope in the middle of execution. Practitioners should stop mapping agent behaviour to static service-account thinking and instead govern the runtime decision surface.
A few things that frame the scale:
- Only 10% have a strategy for managing those agents as identities, according to the 2026 Infrastructure Identity Survey.
- A separate finding shows 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years.
A question worth separating out:
Q: Who is accountable when an AI agent takes an unauthorized action?
A: Accountability should follow the delegation chain, not just the final API call. The relevant parties are the human or system that delegated authority, the owner of the agent, and the team that defined the access policy. If the chain is unclear, the governance model is already incomplete.
👉 Read our full editorial: AI agent identity security is outpacing traditional IAM controls
