Topic starter
23/03/2026 4:04 pm
TL;DR: AI agents are increasingly authenticating into services, chaining actions, and operating with real authority, which turns them into machine-speed identities that existing IAM models often cannot inventory or govern effectively, according to Token Security. The control problem is no longer agent usefulness but access scope, autonomy, and ownership discipline.
Editorial analysis by NHI Mgmt Group, based on content published by Token Security: “Introducing The Agentic Pulse: What We’re Learning About AI Agents (and Why It Matters)”.
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 AI permissions create more risk when they inherit access from other systems?
A: Inherited access is risky because the AI layer often borrows trust without inheriting the original governance discipline.
Q: How can teams tell whether AI access is actually under control?
A: Look for evidence that access is limited by purpose, not just by account.
Practitioner guidance
- Define AI agents as governed identities Add AI agents to your identity inventory with an owner, a purpose, an access scope and a review path, rather than letting them remain implicit tools inside other accounts.
- Separate inherited access from assigned access Track permissions inherited from the human user separately from permissions granted directly to the agent, especially for local agents that operate inside employee environments.
- Bound production agent delegation Document the service identity, credential source and external inputs that allow a production agent to act, then limit the chain of authority to the minimum required for the workflow.
Bottom line: AI agents are becoming governed identities in their own right, not just new interfaces sitting on top of existing accounts.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 4:29 pm
AI agent identity is no longer a future category, it is an operational one. The article shows that agents are already authenticating into systems, inheriting permissions and taking actions with business impact. That means IAM programmes are not dealing with a hypothetical class of tooling but with a live identity boundary that must be governed as such. The practitioner conclusion is straightforward: if the actor can act independently and hold access, it belongs in the identity model.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
A question worth separating out:
Q: Should organisations prioritise access scope or autonomy when governing AI agents?
A: Access scope should come first because it is the control surface teams can define and audit today. Autonomy matters, but reducing it can also reduce the business value the agent was introduced to deliver. The better sequencing is to constrain access tightly, then allow only the level of autonomy that the use case genuinely needs.
👉 Read our full editorial: AI agent identity risk is outpacing enterprise IAM controls