TL;DR: AI agents are breaking identity models built for people, because they act with real access, independent judgment and machine speed while most organisations still cannot prove who acted on what authority, according to Newcore. The accountability gap means agent identity, ownership and action-level governance now matter as much as human access reviews.
Editorial analysis by NHI Mgmt Group, based on content published by Newcore: ““It’s Not Me, It’s My Agent”: Governance and Accountability in the Age of AI Agents”.
Key questions
Q: What breaks when AI agents are reviewed like human users?
A: Human review assumes access is stable long enough to be observed, approved, and recertified.
Q: Why do AI agents create accountability problems for IAM and NHI teams?
A: AI agents create accountability problems because traditional IAM proves who authenticated, while agent governance must prove what the actor did with that access.
Q: How do organisations decide whether an AI agent should be allowed to act autonomously?
A: Autonomy should be granted only when the business process can tolerate runtime action, the owner is clear, the policy engine can make immediate decisions, and the activity is fully attributable.
Practitioner guidance
- Inventory agents as first-class identities Discover agents across platforms, assign each one a unique owner and keep that owner current through role changes and offboarding.
- Derive agent authority from the human owner Ensure an agent cannot exceed the access of the person it represents, and transfer ownership rather than orphaning the agent when staff move or leave.
- Govern actions instead of applications Define which actions are allowed, which require human approval and which are denied outright, especially for bulk updates, exports and irreversible changes.
Bottom line: AI agents are exposing a gap in identity programmes that were designed for people, not for actors with independent judgment and machine-speed execution.
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