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Most organisations have a risk register covering financial, cyber, and operational exposures — but many have yet to add a single entry for AI. That's the gap this publication from Acuity AI Advisors sets out to close. The AI Risk Register framework covers six core risk categories every organisation needs to address: data risk (poor data governance flows directly into AI outputs), model risk (inaccuracy, hallucination, drift, and automation bias), regulatory risk (existing laws already apply, and AI-specific regulation is accelerating), reputational risk (AI failures rarely stay internal), operational dependency risk (when AI shifts from enhancement to assumption), and third-party and vendor risk (adopting someone else's AI doesn't transfer the accountability).
What makes this framework compelling is its central argument: AI risk isn't a special case requiring entirely new thinking — it responds to the same disciplines as any other enterprise risk. Structured identification, clear ownership, proportionate controls, and regular oversight. The AI Risk Register is the mechanism that turns vague awareness into managed exposure, giving boards the visibility they need and management the accountability structures to act. As the authors put it, the risk you can see is the risk you can manage — and right now, too many organisations are governing AI with their eyes closed.
