Managing AI Risk in Data–Driven Transformation

A structured governance framework for managing AI risks in data-driven transformations Traditional risk frameworks fail to address the challenges AI introduces into change programs: probabilistic assessment breaks down under Knightian uncertainty, emergent system behaviour defies prediction, and human-in-the-loop oversight too often provides psychological rather than functional protection. Managing AI Risks in Data-Driven Transformations responds with the Human Firewall, an original governance framework that moves the term beyond its familiar cyber-security usage into a rigorous, three-pillar model of AI oversight, integrating ethical stewardship, institutional accountability, and behavioral assurance. The book translates these pillars into auditable practice through a suite of practical instruments: the H-E-V-R risk assessment framework (Hazard, Exposure, Vulnerability, Response), the AI Ethics Risk Register, the Trust Impact Matrix, and the six-step Risk-Ethics Integrated Assessment (REIA) methodology, all aligned to the NIST AI Risk Management Framework, ISO/IEC 42001:2023, IEEE 7000-2021, and the EU AI Act. Governance models, templates, reflective questions, and sector-based case studies from financial services, healthcare, and the public sector demonstrate how to navigate technical and organisational risks in an integrated way. The book also covers: Why conventional probabilistic risk assessment is structurally inadequate for AI, and how automation bias and selective adherence undermine human oversightA new AI risk taxonomy addressing algorithmic opacity, bias, model drift, and hallucination, with trust calibration as the governance objectiveAccountability architectures for AI Ethics Boards and stewardship committees, including the Three Lines of Defence adapted for AI and contestability rights under the GDPR and EU AI ActCapability building from classroom to boardroom: AI literacy, certification pathways, and an AI Risk Leadership FrameworkAI governance maturity model and adaptive governance strategies for regulation and technology that continue to evolve Designed for project and program managers, risk and governance professionals, and board directors leading AI-driven change, this book also supports professionals pursuing ChPP, CEng, and related chartered and certification pathways. It functions as a graduate textbook for MBA, MSc AI, Data Science, and Technology Management programs, and as a resource for executive education and professional CPD.