Topic starter
25/06/2026 2:48 pm
TL;DR: AI agents are being treated as a new workload class, which pushes identity teams to govern them with machine identity, lifecycle, and access controls rather than human-centric IAM patterns, according to Keyfactor. The governance problem is that autonomy and runtime tool use can outpace review cycles, making static assumptions about privilege persistence increasingly brittle.
Editorial analysis by NHI Mgmt Group, based on content published by Keyfactor: “AI Agents Are the New Workload: What That Means for Security”.
Key questions
Q: What breaks when AI agents are treated like standard human users?
A: You lose visibility into effective permissions, expected behaviour, and real blast radius.
Q: Why do autonomous AI systems create more identity risk than normal automation?
A: Normal automation follows a fixed path, but autonomous systems can interpret goals, choose actions, and continue without waiting for a person.
Q: How should security teams set access boundaries for AI agents?
A: Security teams should define agent boundaries around data sources, tools, and allowed actions before any production rollout.
Practitioner guidance
- Define agents as governed workload identities Create a separate identity class for AI agents with explicit ownership, approval path, and credential scope.
- Tie access to task boundaries Issue credentials for the narrowest execution window possible and revoke them when the task or workflow completes.
- Inventory agent instances and their credentials Maintain an inventory of every deployed agent, its owner, attached secrets, and the systems it can reach.
Bottom line: AI agents should be governed as workload identities because their access behavior is runtime-driven, not human-like and not stable.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
25/06/2026 3:57 pm
AI agents are emerging as workload identities, not just smarter users. That means their governance should be anchored in machine identity discipline rather than human access assumptions. When the subject can call tools and act at runtime, the operational question becomes who owns the workload, what it can reach, and how its access is bounded. Security teams should stop mapping agent behaviour onto user-centric IAM patterns that were never designed for autonomous execution.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should organisations do when AI agent privileges change after deployment?
A: They should treat any new plugin, connector, or API integration as a change to the agent’s security posture and revalidate the effective capability set. If the new access exceeds the approved purpose, the agent should be restricted until the gap is reviewed and corrected.
👉 Read our full editorial: AI agents are becoming workloads, and IAM must adapt