The Governance Stack — Data Truth, Model Risk, and the Authority Layer Nobody Built
Most organisations have some data governance and emerging AI governance. Almost none have built the third layer — the one regulators will actually ask about. SF
Most organisations have some data governance and emerging AI governance. Almost none have built the third layer — the one regulators will actually ask about. SF
Large AI verdicts are ungovernable. The fix isn’t better explanations — it’s smaller decisions wrapped in deterministic code.
Most AI governance fails at runtime. Not because policies are missing. Not because principles haven’t been documented. Because at the moment a decision executes, nothing technically prevents an unauthorised action from going through. You have governance frameworks. You have monitoring dashboards.
LLM providers protect reasoning tokens with cryptographic verification. They don’t verify conversation history. That gap is where attacks live.
Scanning for malware isn’t security. Proving who authorised the action is.
We need an HR seat on AI governance. Not as a courtesy. As a structural requirement.
Your AI outputs are generic because there’s no supply chain feeding the right context at the right time — not because your model is dumb.
Why the ‘safe’ AI project is often the boss fight — and a 7-question test to pick winners instead.
Paying expensive maintenance has always been cheaper than replacement. AI just flipped the economics.
Why prompt-based guardrails will always fail — and what actually works