Topic starter
15/05/2026 7:34 pm
TL;DR: Delegation chains can move across systems while staying inside existing permissions, Omada Identity says, which leaves authorised scope unproven when agents make decisions. Governance needs runtime evidence that ties each step back to approved authority.
Editorial analysis by NHI Mgmt Group, based on content published by Omada Identity: “Governing AI Agents: What Changes When Identities Make Decisions”.
Key questions
Q: Why do AI agents require runtime evidence instead of only access logs?
A: Access logs capture activity, but they do not explain why a chain of agent actions was authorised or whether the resulting behaviour stayed within scope.
Q: How can security teams tell whether AI agent access is drifting out of scope?
A: Look for agents touching systems, data sets, or tools that are outside the intended task boundary, especially when those actions are not part of the approved workflow.
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.
Practitioner guidance
- Define an authorization scope for every agent Document the agent’s business purpose, accountable owner, allowed tools, data domains, permitted actions, prohibited actions, and review cadence before production use.
- Map delegation chains end to end Trace how an agent uses credentials, tools, and downstream identities across systems so one review can cover the full decision path.
- Compare runtime behaviour to approved scope continuously Use operational telemetry from cloud platforms, SaaS applications, IAM, and SIEM to detect when an agent begins acting outside its defined authority.
Bottom line: AI agent governance fails if it remains account-centric, because the real risk is unauthorised decision-making inside existing permissions.
Glossary terms: Defined Authority, Delegation Chain, Runtime Drift, Decision Evidence
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This topic was modified 18 hours ago by NHI Mgmt Group
15/05/2026 7:35 pm
Defined authority is the missing governance primitive for agentic behaviour: Traditional identity programmes can describe who has access, but they do not always describe what an AI agent is authorised to decide. That gap matters because agents can interpret context and compose actions at runtime. The practical conclusion is that governance for autonomous identities starts with a documented authority boundary, not with another generic access review.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should teams do when an AI agent’s delegation chain crosses multiple systems?
A: Treat the full chain as one governance decision rather than separate local approvals. Each hop should be traceable to the same sponsor, purpose, and approved scope, with an escalation path when the combination would not be acceptable if requested directly. That is how separation of duties survives agentic execution.
👉 Read our full editorial: AI agent governance needs defined authority and runtime evidence
This post was modified 18 hours ago by NHI Mgmt Group