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22/06/2026 10:03 am
TL;DR: AI governance often relies on attestations, questionnaires, and framework mappings that say what should be true, but not whether AI systems are actually behaving that way, according to Cranium. The real control gap is the absence of runtime evidence such as model verification, event logs, and telemetry that turns policy into provable reality.
NHIMG editorial — based on content published by Cranium: AI governance needs runtime proof, not green checkmarks
By the numbers:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption.
Questions worth separating out
Q: How should security teams govern AI systems that can change after approval?
A: They should bind approval to runtime evidence rather than to static attestations.
Q: Why do static AI policies fail in practice?
A: Static policies fail because they describe an intended state, while AI environments can change model versions, hosting locations, integrations, and tool access after sign-off.
Q: How can organisations tell whether AI governance is actually working?
A: They should look for evidence that approvals, logs, and observed behaviour reconcile over time.
Practitioner guidance
- Require runtime evidence for every AI approval Link each approved model or agent to logs that verify the active version, data path, and tool usage after deployment.
- Replace static vendor cards with living trust records Maintain a record that continuously reconciles declared posture with observed behaviour, including model changes, policy drift, and event history.
- Instrument prompt and agentic telemetry by default Capture prompt inputs, tool calls, and execution outcomes for AI systems that influence decisions or access.
What's in the full article
Cranium's full article covers the operational detail this post intentionally leaves for the source:
- The article explains how model cards, attestations, and questionnaires fail when the deployed system changes after approval.
- It outlines the runtime evidence layer, including model probing, event logs, and prompt or agentic telemetry.
- It introduces the AI Card as a living trust record that reconciles declarations with observed behaviour.
- It shows why governance without sensors becomes compliance theatre instead of enforceable control.
👉 Read Cranium's analysis of why AI governance needs runtime proof →
AI governance credibility gap: are your controls proving reality?
Explore further
22/06/2026 10:46 am
AI governance without runtime verification is documentation theatre. The article is right to separate policy from proof, because most governance stacks still reward paperwork more than observed control. A model card, questionnaire, or framework mapping can describe intent, but it cannot confirm that the deployed model, data path, or toolchain still matches that intent. The implication is that governance programmes must stop treating static attestations as evidence of security.
A few things that frame the scale:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to The 2026 Infrastructure Identity Survey.
- 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.
A question worth separating out:
Q: Who is accountable when an approved AI system drifts from its declared posture?
A: Accountability should sit with the teams that own approval, monitoring, and change control together, not with compliance alone. If the system changes after approval and nobody can prove when the change occurred, then governance failed at the control boundary. AI risk management frameworks and identity governance both depend on that boundary being observable.
👉 Read our full editorial: AI governance needs runtime proof, not green checkmarks
