Enterprise-Wide AI Risk Management (Ew-Airm): A Practical Framework for Managing AI Risks in Any Organisation
A structured governance framework for managing AI risk across your entire organization
Enterprise-Wide AI Risk Management (EW-AiRMTM): A Practical Framework for Managing AI Risks in any Organisation, by Prof. Markus Krebsz, introduces a multi-layered governance framework forged from five years of United Nations participatory AI policy development and over thirty years of risk management experience. EW-AiRMTM augments existing ERM frameworks like ISO 31000, COSO, and the UK Orange Book with AI-specific governance capabilities.
The book presents a risk taxonomy drawn from more than 1,700 categorised AI risks across seven evidence-based domains and 24 sub-domains, alongside over 800 risk mitigation controls organised into four actionable quadrants. Six foundational pillars establish governance prerequisites before AI deployment, while the HAiPECR ethical filter keeps human accountability central to every governance decision.
Readers will also find:
A resilience framework designed to prepare organisations for AI Black Swan events that cannot be fully anticipated in advance Practical guidance on extending existing enterprise risk management structures rather than replacing them with entirely new governance architectures A risk taxonomy spanning seven domains and 24 sub-domains built from over 1,700 categorised AI risk scenarios The HAiPECR ethical framework, hosted on the OECD.AI tool website, embedding human accountability into every governance decisionWritten for Chief Risk Officers, board members, compliance leaders, data scientists, and senior executives making AI deployment decisions, this book delivers a field-tested governance framework that addresses the AI-specific risks existing enterprise risk management structures were never designed to handle.
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