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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Managing AI Risks in Data-Driven Transformations

Managing AI Risks in Data-Driven Transformations

Beg, Sayara

Hardcover

Technology & EngineeringGeneral Computers

PREORDER - Expected ship date December 16, 2026

ISBN10: 1394406819
ISBN13: 9781394406814
Publisher: Wiley-IEEE Press
Published: Dec 16 2026
Pages: 752
Language: English

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 oversight
  • A new AI risk taxonomy addressing algorithmic opacity, bias, model drift, and hallucination, with trust calibration as the governance objective
  • Accountability 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 Act
  • Capability building from classroom to boardroom: AI literacy, certification pathways, and an AI Risk Leadership Framework
  • AI 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.

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