Topic starter
25/08/2026 6:10 pm
TL;DR: Identity security is shifting from static least-privilege policy to runtime governance for AI agents, shadow AI, and cloud-connected access paths, with a practical focus on discovery, control, and lifecycle management across Microsoft Copilot Studio, Bedrock, Azure AI Foundry, and Vertex AI, according to Veza. The central issue is that AI agent identity assumptions break when tools, permissions, and execution timing are no longer human-paced or predictable.
Editorial analysis by NHI Mgmt Group, based on content published by Veza: “NHI”.
Key questions
Q: What breaks when least privilege is designed before an AI agent starts working?
A: What breaks is the assumption that the needed scope is knowable in advance.
Q: Why do shadow AI tools create identity governance risk?
A: Shadow AI is risky because users often reach those tools through identities, browser sessions, or tokens that were never assessed for data handling or access scope.
Q: How can organisations tell whether AI agent governance is actually working?
A: Look for evidence that agent access is ephemeral, traceable, and constrained at the action level.
Practitioner guidance
- Inventory AI agents as governed identities Create a formal register for every agent, workflow assistant, and shadow AI path that can reach production tools or data.
- Reassess least privilege at runtime Move from static role assignment to task-scoped permissions that are evaluated when the agent requests tools or data.
- Tie offboarding to downstream tool access When an agent is retired, revoke its direct credentials and any delegated access it acquired in connected services, APIs, and cloud platforms.
Bottom line: AI agents change least privilege from a static provisioning concept into a runtime governance problem.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 19 hours ago by NHI Mgmt Group
25/08/2026 6:11 pm
Least privilege is no longer a provisioning rule when the actor is an AI agent. It was designed for access scopes that remain stable long enough to review, certify, and revoke on schedule. That assumption fails when an agent can choose tools and execute actions at runtime, which means the real control point moves to issuance, runtime monitoring, and task boundary enforcement.
A few things that frame the scale:
- Gartner predicts that more than 50% of successful cyberattacks against AI agents through 2029 will exploit access control weaknesses.
A question worth separating out:
Q: What happens when an AI agent completes a task but nobody revokes its access?
A: When access is not revoked, the agent can keep running with valid credentials even after its business purpose is gone. That turns a temporary automation into standing access with no active owner. Over time, the agent can be forgotten, reused in ways nobody intended, or discovered only during audit or incident response, when the exposure has already accumulated.
👉 Read our full editorial: AI agent identity security and least privilege maturity
This post was modified 19 hours ago by NHI Mgmt Group