Topic starter
19/08/2026 3:53 pm
TL;DR: AI agents can request and use access in ways that exceed their original provisioning, and Fiddler’s article argues that existing permission models, bearer tokens, and static grants are not designed for that behaviour. The core issue is assumption collapse: access review and fixed-scoping models presume stable permissions, while agents can seek new access mid-task and expose secrets through context.
Editorial analysis by NHI Mgmt Group, based on content published by Fiddler: “AI Agents Have an Identity Complex”.
Key questions
Q: What breaks when AI agents have broader access than their tasks require?
A: Over-privileged agents break segregation of duties, weaken auditability, and expand blast radius across transactions, data lookups, and workflow triggers.
Q: Why do static secrets create more risk for AI agents than for traditional workloads?
A: Static secrets create more risk for AI agents because agents act autonomously, at high speed, and often across multiple systems.
Q: What are the signs that an AI agent permission model is failing in practice?
A: Common signs include agents requesting access beyond their role, attempting sensitive actions without clear approval, or operating with no audit trail for who approved what.
Practitioner guidance
- Define runtime permission boundaries Map where AI agents are allowed to request new access during execution and where they must stop and re-authenticate against policy.
- Keep credentials out of model context Route secrets through proxies or gateways so bearer tokens, recovery codes, and API keys never appear in LLM prompts or outputs.
- Shorten credential lifetime for agent use Prefer short-lived OAuth access tokens with narrow scope for tasks that need delegated access, especially where the agent can act autonomously.
Bottom line: AI agents expose a structural mismatch in existing permission models because they can request new access while executing work.
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19/08/2026 4:30 pm
Access review is not a sufficient control when the actor can expand privilege mid-task: review cadences assume access persists long enough to be observed, certified, and revoked. AI agents can request additional permissions during execution, which means the thing being reviewed may not be the thing that was originally granted. The implication is that governance must shift from static entitlement review to runtime authorisation boundaries.
A few things that frame the scale:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: How should teams govern access when AI agents and service accounts share the same business systems?
A: Treat them as different identity subjects with the same governance obligation. Create one access model that covers ownership, entitlement scope, review cadence, and offboarding across human and non-human identities, then apply role-appropriate controls to each class. The goal is not separate programmes. It is one risk model that can follow access across systems and workflows.
👉 Read our full editorial: AI agent identity exposes the gaps in existing permission models