Leading AI Transformation

Principal Microsoft cybersecurity leader Yuri Diogenes delivers a practical playbook for leading enterprise AI transformation with strategy, governance, and secure Microsoft AI adoption! Lead with confidence through the full lifecycle of enterprise AI adoption with Leading AI Transformation: Strategy, Governance, and Security Considerations by Yuri Diogenes. Written for business and technology leaders who need to move beyond isolated pilots, this practical guide shows how to defi ne an AI vision aligned to business outcomes, establish responsible AI governance, and scale solutions securely using Microsoft technologies such as Microsoft 365 Copilot, Microsoft Foundry, Azure OpenAI in Foundry Models, and others. Grounded in real enterprise scenarios and decision frameworks, the book helps executives, architects, and security leaders speak the same language—so organizations can accelerate AI transformation while managing risk, compliance, and end-to-end security. By reading this book, you will: Identify where generative AI can deliver measurable business value (and where it can’t)Build an implementation and adoption strategy that accounts for people, process, and operating model readinessEstablish responsible AI governance principles and an AI council for oversight and alignmentDesign identity-first security for AI using Zero Trust concepts and Microsoft Entra controlsProtect and govern data used by AI solutions with Microsoft Purview, including discovery, classification, and DLPCreate a repeatable path from experimentation to production, without sacrificing trust, compliance, or securityPrepare for Exam AB-731: AI Transformation Leader with objective-aligned guidance and review support About This Book For executives, directors, transformation leaders, and consultants who want to move beyond AI pilots and deliver measurable business value with a clear strategy, governance model, and secure execution approachFor security, risk, compliance, and data governance leaders, and business stakeholders, who need to scale Microsoft AI solutions while protecting identity, data, cloud workloads and meeting responsible AI expectations