Topic starter
14/10/2026 3:49 pm
TL;DR: Treating AI agents as ordinary non-human identities creates over-permissioned, hard-to-audit access because agents decide at runtime, act on behalf of people, and can be steered by prompt injection, according to Newcore. The right model is task-scoped delegation, human sponsorship, and lifecycle controls built for agent behaviour, not static workloads.
Editorial analysis by NHI Mgmt Group, based on content published by Newcore: “Your Agents Aren't Service Accounts: Why NHIs Are the Wrong Model for AI Agents”.
Key questions
Q: What breaks when AI agents inherit access from users and service accounts?
A: The main failure is that inherited access can be broader than the agent’s actual task, so privilege becomes easier to reuse than to govern.
A: Because the failure is semantic, not technical.
Q: What are the signs that an agent identity model is failing in practice?
A: Look for shared credentials, unclear ownership, agents that inherit the union of multiple tasks, and logs that identify the agent but not the requesting user.
Practitioner guidance
- Define agents as a separate governed identity class Create a policy category for agents that is distinct from service accounts and ties each agent to a named human sponsor, approved task scope, and explicit ownership record.
- Replace standing access with task-scoped delegation Issue short-lived, task-specific tokens for each agent run so the effective authority mirrors the sponsoring user and expires when the task ends.
- Capture the full delegation chain in logs Log the initiating user, the agent, any sub-agents, the tools called, and the task context so investigators can reconstruct who authorised what and where the action flowed.
Bottom line: Treating AI agents as ordinary NHIs creates a mismatch between static identity controls and runtime decision-making.
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14/10/2026 3:50 pm
AI agents are not just another NHI class, because their authority changes at runtime. NHIs were built for stable workloads with a fixed purpose, but agents can switch tasks, tools, and outcomes inside one session. That means the classic provisioning model no longer describes the security problem correctly. Practitioners need to stop asking only what the credential can reach and start asking how authority is formed while the session is still live.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern agent actions that affect sensitive systems?
A: Use sponsor-bound delegation, just-in-time authority, and explicit approval points for high-risk actions such as data deletion, access changes, or money movement. The key is to keep the agent's effective permissions no broader than the human request that initiated the task. Where the task is ambiguous, the governance model should force re-authorisation before the action completes.
👉 Read our full editorial: AI agents need a distinct identity model beyond NHI inventory