AI Risks: Proven Detection and Governance Strategies

AI has moved out of the lab and into pricing engines, hiring funnels, fraud screens, customer conversations, and board-level decisions. Often, it arrives quietly through a pilot, a vendor feature, or a free trial. Then it starts shaping outcomes that affect customers, employees, regulators, and shareholders.

AI Risk: Proven Detection and Governance Strategies is a practical field guide for leaders who must detect, assess, govern, and evidence AI risks before they become audit findings, compliance failures, operational surprises, or trust problems.

Written without jargon, the book treats AI risk as it functions in business: not only as a technical model issue, but as a decision, governance, assurance, and accountability challenge. Across eight chapters, Maman Ibrahim shows how to map your AI risk frontier, assess data and model risks, challenge suppliers, design controls, and build an operating model that can withstand executive and regulatory scrutiny.

With case studies, diagnostics, scorecards, checklists, and templates, this is a practical guide for CISOs, CROs, audit leaders, board directors, legal counsel, technology executives, students, and managers who need to govern AI with confidence.

See the risk early. Govern it well.

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