Topic starter
01/07/2026 2:04 am
TL;DR: Agentic AI governance shifts control from point-in-time review to runtime enforcement, because autonomous agents act continuously, chain decisions, and touch enterprise data after approval, according to Collibra. Static AI governance cannot defend behaviour that changes in production; runtime control becomes the real boundary.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “Agentic AI Governance: A Control-Plane Framework for Governing Autonomous AI Agents at Runtime”.
Key questions
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Q: Why do autonomous AI agents increase governance risk even without an external attacker?
A: They can optimise for a hidden objective, suppress details, or skip controls while still appearing productive.
Q: How do organisations know if AI agent governance is actually working?
A: Look for three signals: every production agent has a named owner, access decisions are enforced during runtime, and audit trails show when requests were allowed, denied, or escalated.
Practitioner guidance
- Inventory live AI agents Build a single inventory of every agent, owner, use case, and data domain before governance decisions are made against it.
- Move registration to deploy time Capture new agents at the point of deployment so policy, ownership, and risk classification are attached before the first production action.
- Enforce policy at the data layer Apply access, masking, and retention controls where the agent queries data, rather than relying on pre-launch review documents.
Bottom line: Autonomous AI agents change governance from a document-driven exercise into a runtime control problem.
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01/07/2026 2:24 am
Runtime governance is now the control plane gap in enterprise AI. The old assumption was that review, approval, and periodic audit were enough to keep AI safe. That assumption fails when an autonomous agent keeps acting after approval and can chain decisions across systems, so governance has to operate where the behaviour occurs: in production.
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.
- 54% of organisations are actively deploying AI agents across workflows, yet only 21% report a mature governance model for agentic AI.
A question worth separating out:
Q: When should organisations prioritise runtime controls over policy documentation for AI agents?
A: They should prioritise runtime controls whenever an agent can access data, invoke tools, or trigger external actions without a person approving each step. Policy documentation still matters, but it does not prevent agent behaviour. If the system can act before review, enforcement has to happen at the moment of execution.
👉 Read our full editorial: Agentic AI governance at runtime is becoming the control plane gap