Compliance and Regulation for AI Cybersecurity MTA
Navigating Law, Standards, and Certification in an AI-Driven World

About this book:

This book, "Compliance and Regulation for AI Cybersecurity," serves as a comprehensive guide for organizations navigating the complex intersection of artificial intelligence, cybersecurity, and global regulatory frameworks. It moves beyond theoretical concepts to provide actionable strategies for operationalizing AI security and demonstrating compliance across the entire AI lifecycle. The book highlights that AI introduces unique risks, such as data poisoning, adversarial evasion, and prompt injection, which necessitate specialized security controls and a departure from traditional cybersecurity approaches.

The text delves into foundational legal and standards frameworks, including the EU AI Act, NIST AI Risk Management Framework, and ISO/IEC standards (27001, 27701, 42001, and 23894). It explains how these regulations and standards mandate a "security-by-design" approach, requiring organizations to integrate security and ethical considerations from the initial data collection and model development phases through to deployment and ongoing monitoring. Key areas covered include secure ML development lifecycles, robust data governance for AI, model and supply chain security (emphasizing SBOMs and vendor risk management), and sophisticated logging, monitoring, and identity/access management tailored for AI workloads.

A significant portion of the book is dedicated to sector-specific playbooks, translating general AI compliance into practical guidance for highly regulated industries like healthcare (HIPAA, HITECH, FDA SaMD AI), financial services (GLBA, DORA, PCI DSS), critical infrastructure and energy (NERC CIP, NIS2), and the public sector/defense (FedRAMP, CMMC, federal mandates). These sections detail how existing regulations are being reinterpreted and expanded to cover AI-specific risks and obligations, including cross-border data transfer challenges like GDPR and data localization laws.

Finally, the book emphasizes the critical importance of independent validation through audits and assessments, including SOC 2 reports, ISO certifications, and regulatory examinations. It details strategies for preparing for these reviews, managing audit findings, and leveraging continuous compliance automation, policy-as-code, and GRC platforms to maintain an auditable and continuously assured AI posture. The concluding chapter focuses on building a strategic roadmap, utilizing AI cybersecurity maturity models, defining measurable KPIs and KRIs, and effectively reporting on AI risk and compliance to executive leadership, underscoring that AI trustworthiness is a continuous journey requiring adaptive strategies and transparent communication.

What You'll Find Inside:

• Comprehensive coverage of global AI regulations including the EU AI Act, NIST AI RMF, and ISO/IEC standards (27001, 27701, 42001, 23894) with practical implementation guidance
• Detailed exploration of AI-specific threats like data poisoning, adversarial evasion, and prompt injection, along with defensive strategies and secure ML development lifecycle practices
• Sector-specific compliance playbooks for healthcare (HIPAA, FDA SaMD), financial services (GLBA, DORA, PCI DSS), critical infrastructure (NERC CIP, NIS2), and public sector/defense (FedRAMP, CMMC)
• Practical guidance on building audit-ready AI systems through evidence collection, continuous compliance automation, control mapping, and certification pathways (SOC 2, ISO)
• Strategic framework for AI cybersecurity maturity, including KPIs/KRIs, roadmap development, and board reporting to demonstrate continuous trust and adaptive compliance

Who's It For:

This book is for CISOs, compliance and risk leaders, ML and data engineers, product managers, in-house counsel, and auditors who need to implement AI security controls, understand regulatory requirements, and demonstrate compliance. It assumes familiarity with cybersecurity fundamentals while providing the AI-specific depth needed to make defensible choices in regulated industries such as healthcare, financial services, critical infrastructure, and public sector/defense.