AI Risk Management and Governance

The 5 risks that actually matter to a board

• Data leakage — confidential or regulated data going into public tools.
• Accuracy & hallucination — decisions made on plausible-sounding but wrong output.
• Bias & fairness — AI perpetuating or amplifying historic bias, especially in HR, credit, or service.
• Legal & regulatory — copyright, sector-specific rules (financial services, healthcare, public sector), and emerging AI laws.
• Reputational — the customer-facing AI mistake that ends up in a screenshot on social media.

The 1-page AI governance policy

You don't need a 40-page document. You need a page that answers:

1. Approved tools — this list, and no others without approval.
2. What data can go into them, and what can't (with examples).
3. Human-in-the-loop — where a person must sign off before AI output is acted on.
4. Disclosure — when we tell customers AI was used.
5. Who to ask — one named owner for AI questions.

The governance stance that unlocks speed

Frame it as "here's how to use AI safely", not "here's what you can't do." Same policy, opposite adoption curve.

Try it now

"Draft a one-page AI governance policy for my organization covering data, accuracy, bias, legal exposure and approved tools. Ask me about our sector and regulatory context first."

Take the draft to Legal, Risk, and IT. You've just skipped the hardest part — the blank page.