The book explores the AI risk universe, highlighting how unreliable data, flawed assumptions, model limitations, incorrect outputs, historical bias and excessive reliance on automated decisions can lead to unintended consequences. It explains why model validation alone is insufficient, how risks multiply at scale, and why a successful pilot does not guarantee safe enterprise-wide adoption.

Moving from risk emergence to risk management, the book examines adaptive governance, accountability, the Three Lines model, AI risk appetite, the evolving role of the Chief Risk Officer and meaningful human oversight. It encourages leaders to question AI-generated recommendations, test underlying assumptions and recognise when changing conditions require intervention.

A key contribution is the Seven-Stage AI Risk Intelligence Framework, which guides organisations through identifying, classifying, assessing, governing, approving, monitoring and reassessing AI applications throughout their lifecycle.

The Book is Available on Amazon and Notion Press