Topic starter
03/02/2026 4:27 pm
TL;DR: AI agents move beyond predictable delegated authorization into runtime reasoning, tool chaining, and workload-style identities, which changes how access is granted and trusted according to Andromeda Security. The core issue is that existing IAM assumptions were built for predefined requests and human-paced approval, not dynamically chosen action paths.
Editorial analysis by NHI Mgmt Group, based on content published by Andromeda Security: “AI Agents and Identity: From Delegated Authorization to Agentic Trust”.
Key questions
Q: Where does delegated authorization fail for AI agents?
A: It fails when the actor can choose its own tool sequence at runtime rather than follow a fixed app-to-app request.
Q: Why do AI agents change infrastructure identity governance?
A: AI agents change the model because they can take actions, call tools, and operate without a human approving each step.
Q: What breaks when AI agents keep standing credentials?
A: The access model breaks because the agent can continue acting after the human has moved on, the workflow has shifted, or the original approval is no longer relevant.
Practitioner guidance
- Inventory AI agent identities separately Classify autonomous agents, delegated copilots, service accounts, and human users as distinct identity types so their controls are not blended into one access model.
- Map runtime tool paths Document which tools, APIs, and secondary agents each agent can discover or invoke at runtime, then limit that surface to the smallest credible workflow.
- Require attestation before trust decisions Bind agent access to attested runtime state and approved workload context instead of relying only on a static token or inherited user approval.
Bottom line: AI agents turn identity governance into a runtime trust problem because the actor can choose actions, tools, and execution order after access is issued.
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This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 6:58 pm
Delegated authorization was designed for known actions, not agent-chosen execution paths. The pre-agent model assumes that identity can be authenticated, a registered app can be checked, and a token can safely represent a bounded request. That assumption fails when the actor decides in real time which systems to call and in what order. The implication is that identity governance has to stop treating all non-human access as one class of delegated application behaviour.
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.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern autonomous agents differently from copilots?
A: Autonomous agents need stronger oversight because they can call tools, retrieve secrets, and generate sub-agents without the same session-bound limits as a copilot. Governance should reflect that difference in monitoring, approval, and scope controls rather than using one generic policy for every AI capability.
👉 Read our full editorial: AI agent identity shifts from delegated access to agentic trust