Topic starter
01/12/2025 1:03 pm
TL;DR: AI agents now authenticate into production systems using shared service accounts, long-lived API tokens, and scattered credentials, creating identities that are hard to trace or baseline, according to Token Security’s webinar analysis. Zero Trust can still apply, but only if organisations rebuild identity, lifecycle, and audit controls around agent behaviour, not human assumptions.
Editorial analysis by NHI Mgmt Group, based on content published by Token Security: “When an AI Agent Logs In: A Zero Trust Story Every CISO Is Now Living”.
Key questions
A: Treat the agent as an identity surface, but treat the service account as the source of effective privilege.
Q: Why do long-lived user tokens create governance risk for AI agents?
A: Long-lived tokens assume access remains valid until a person revokes it, but agent behaviour changes the risk model because the actor can decide and act at runtime.
Q: What breaks when AI agents are managed like ordinary machine identities?
A: What breaks is the assumption that access scope can be fully understood from provisioning data and quarterly review.
Practitioner guidance
- Define agent identities explicitly Assign each AI agent a distinct identity record, owner and lifecycle state so logs, approvals and revocation actions map to one runtime actor rather than a shared backend account.
- Replace standing secrets with short-lived credentials Move agents off static API keys and long-lived service account tokens, then scope each credential to a narrow task boundary with automated expiry and revocation.
- Baselining agent behaviour at issuance time Capture the normal tool set, data domains and action patterns for each agent before broad production use, so drift is visible when the agent departs its intended boundary.
Bottom line: AI agents create a governance gap when they log in through shared credentials that hide the actual actor behind the action.
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This topic was modified 10 months ago by Abdelrahman
This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 4:36 pm
AI agent identity is not a variant of human IAM, it is a distinct governance object. The article shows that when a runtime actor can decide, act and chain tools without human intervention, the usual identity assumptions no longer describe the thing being governed. Ownership, attribution and lifecycle control must attach to the agent instance, not just the backend credential. Practitioners should stop treating agent access as a side effect of automation and start modelling it as a first-class identity domain.
A few things that frame the scale:
- 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: What should teams do when an AI agent is retired or replaced?
A: Teams should treat retirement as an access closure event, not a cleanup task. That means revoking credentials, removing service account bindings, confirming that downstream integrations no longer accept the agent’s tokens and checking for orphaned access paths in connected systems. If any binding remains, the agent is not fully offboarded.
👉 Read our full editorial: AI agent identity and zero trust: why IAM models are breaking