Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Cranium: “Why AI Governance is a Myth Without AI Security?”.
Key questions
Q: What breaks when AI governance relies on attestations instead of runtime evidence?
A: Attestations become stale as soon as the system changes.
Q: Why do AI model changes create governance risk even when approvals are still current?
A: Because approval often attaches to a declared model, not to the live service that users actually hit.
Q: How do security teams know if AI governance is working?
A: Look for evidence that access decisions are reviewable, permissions are revocable, and exceptions are not becoming permanent.
Practitioner guidance
- Instrument runtime verification Require model probing, version checks, and event logging so the approved AI posture is continuously compared with live behaviour.
- Tie governance to observed identity Make vendor declarations provisional until the deployed model, jurisdiction, and data path are verified in production.
- Enforce inline policy controls Use policy enforcement points for prompts, data access, and agent actions so violations are blocked rather than merely recorded.
Bottom line: AI governance breaks down when it stops at attestations and mappings that cannot prove what the live system is doing.
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22/06/2026 10:46 am
AI governance without runtime proof is a credibility failure, not a documentation gap. Attestations, questionnaires, and framework mappings can show intent, but they cannot show whether the AI estate actually behaved as approved. Once policy is detached from observed behaviour, the governance function becomes archival rather than control-bearing. The practitioner conclusion is simple: no evidence means no credible control.
A few things that frame the scale:
- 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite, according to the 2026 Infrastructure Identity Survey.
- 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.
A question worth separating out:
Q: How should organisations govern sanctioned and unsanctioned AI use together?
A: They should use one enforcement and monitoring layer that covers approved models, shadow AI connections, prompt handling, and data movement. Splitting governance from detection leaves gaps where unsanctioned systems can operate outside policy while still touching production data. The practical aim is a single evidence trail across the whole AI estate.
👉 Read our full editorial: AI governance needs runtime proof, not green checkmarks