About this book:

The *CISO Playbook for AI Risk Management* is a comprehensive strategic guide designed to help security executives integrate artificial intelligence into the corporate enterprise without compromising security, compliance, or ethics. It establishes that AI risk is fundamentally different from traditional IT risk due to the probabilistic nature of machine learning, which necessitates a shift from standard perimeter defense to protecting the integrity of data pipelines, model artifacts, and automated decision-making. The book advocates for a governance-first approach, utilizing established frameworks like NIST AI RMF, ISO/IEC, and COSO to create a cross-functional operating model that aligns data science, legal, and security teams under a unified RACI (Responsible, Accountable, Consulted, and Informed) structure.

The technical core of the playbook addresses the entire AI lifecycle, from secure data ingestion and provenance to model retirement. It highlights specific adversarial threats such as data poisoning, evasion attacks, and model theft, while dedicating significant attention to the unique vulnerabilities of Large Language Models (LLMs), including prompt injection, data leakage, and hallucinations. To mitigate these risks, the book details the fusion of MLOps and SecOps, recommending automated security gates, rigorous red teaming, and AI-aware monitoring systems that track model drift and behavioral anomalies rather than just infrastructure uptime.

Beyond technical controls, the book emphasizes the importance of managing the AI supply chain and third-party vendor risks through specialized contractual clauses and SLAs that mandate transparency and bias mitigation. It provides a roadmap for organizational maturity, moving from ad-hoc responses to a "quantitatively managed" state where Key Risk Indicators (KRIs) are tied to a defined enterprise risk appetite. This strategic alignment ensures that security is viewed not as a bottleneck, but as a business enabler that protects the company’s intellectual property and brand reputation.

The final sections focus on resilience and communication, offering practical templates for AI-specific incident response and tabletop exercises. By focusing on "storytelling with metrics," the playbook equips CISOs to translate complex algorithmic risks into financial and strategic terms for board-level oversight. Ultimately, the book asserts that successful AI adoption requires a commitment to "Responsible AI"—balancing innovation with human oversight, ethics, and rigorous validation to build long-term trust in intelligent systems.

What You'll Find Inside:

• A detailed AI risk taxonomy that categorizes strategic, operational, security, legal, ethical, and data/IP risks to create a common language for governance.
• Integration of leading frameworks—NIST AI RMF, ISO/IEC standards, and COSO—into a unified AI governance model with clear roles, policies, and decision rights.
• Step‑by‑step guidance for building an AI operating model using RACI matrices, defining accountability, and embedding security into the MLOps lifecycle.
• Practical controls for the full AI lifecycle: data governance, model lifecycle security, MLOps‑SecOps integration, continuous monitoring, and red teaming.
• A metrics‑driven approach to AI risk management, including KPI/KRI development, risk appetite quantification, and board‑level storytelling with scenario‑based analysis.

Who's It For:

This book is written for CISOs, security leaders, and risk officers who must govern and secure AI initiatives across the enterprise. It also serves AI/ML directors, data science managers, compliance officers, and senior executives responsible for AI strategy, providing them with the tools to embed security controls, measure risk, and communicate AI risk posture to the board and business stakeholders.