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AI Security Governance and Assurance

A Comprehensive Guide to Governing, Securing, and Assuring Artificial Intelligence Systems

AI Security Governance and Assurance
This book is 100% completeLast updated on 2026-09-04
Steve Publications

AI is changing fast, and so are the security risks that come with it. This practical guide shows security and technology leaders how to govern, secure and assure AI systems from design through deployment and beyond. Packed with proven frameworks, controls and real-world guidance, it turns complex AI security requirements into practical action.

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About the Book

AI Security Governance and Assurance is a complete reference for security architects, CISOs, AI engineers, governance professionals, and technology leaders who need to govern, secure, and assure AI systems throughout their entire lifecycle. This book covers the principles, architecture, frameworks, strategies, controls, and operational practices required to build effective AI security programs, manage AI-specific risks, implement comprehensive assurance functions, and demonstrate compliance with emerging regulations. Written for practitioners, not theorists, it provides actionable guidance grounded in current standards including the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, OWASP LLM security guidance, and leading industry practices.

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About the Author

Steve Publications

Steve is a technology professional with more than 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. Throughout his career, he has designed, secured, analyzed and tested complex software and infrastructure, with a particular focus on understanding how systems fail and how they can be made more secure.

Outside of work, Steve enjoys sharing knowledge with the technology community. He collaborates with researchers, industry experts and technology professionals to write practical books covering software development, cybersecurity, cloud computing, networking, DevOps, artificial intelligence and enterprise technologies. His books focus on practical learning through clear explanations, real-world examples and hands-on exercises. With more than two decades of industry experience, his goal is to help IT professionals, students and technology enthusiasts build useful skills and stay current in a rapidly changing industry.

We believe readers deserve to know how our books are created. Most of our authors are not native English speakers, so we use AI to help translate, proofread manuscripts, fix grammar, improve sentence structure and make technical explanations easier to read. AI is used as an editing tool only. It does not replace the research, technical knowledge or hands-on experience behind our books. Some of our authors also prefer to remain anonymous for privacy or professional reasons. In those cases, we publish their work under a different name. The author's name may be different, but the quality of the content and our review process remain the same.

Every book is written, reviewed and maintained by experienced technology professionals, with contributions from our private technical community of more than 420 engineers and researchers. We spend far more time validating technical accuracy and keeping our content up to date than generating text. We are always interested in working with experienced professionals who have deep expertise in a particular technology or domain. If you would like to publish a book with us or help review an existing manuscript, we'd love to hear from you. Send us a message describing your area of expertise. We are especially interested in niche technologies, specialized skills and emerging topics that are underrepresented in existing technical literature.

If you look through the contents of our books, you'll see practical examples, detailed explanations and material that is regularly updated. Our goal is to publish books that professionals can actually rely on, not low-effort AI-generated content. If you ever feel that one of our books does not meet that standard, Leanpub offers a 60-day money-back guarantee. Feel free to request a refund if you are not satisfied with your purchase.

Contents

Table of Contents

Introduction

Chapter 1: Why AI Security Governance and Assurance Is Different

  1. Why AI Security Is Different
  2. The Stakes: Real-World Incidents and Failures
  3. From Cybersecurity to AI Security: Evolution of the Discipline
  4. What This Book Covers and How to Use It
  5. The Integrated Approach: Governance, Risk, and Assurance

Chapter 2: Foundations of AI Security and Risk

  1. Core Terminology and Concepts
  2. Understanding AI Risk: Categories and Characteristics
  3. AI-Specific Failure Modes
  4. The Security-AI-Privacy Triangle
  5. Trust, Safety, and Reliability: Beyond Traditional Security
  6. Accountability and Responsibility in AI Systems

Chapter 3: The AI Threat Landscape and Attack Surfaces

  1. Prompt Injection and Indirect Prompt Injection
  2. Data Poisoning and Model Poisoning
  3. Adversarial Examples and Evasion Attacks
  4. Model Extraction, Inversion, and Membership Inference
  5. Insecure AI Supply Chains and Model Dependencies
  6. Excessive Agency and Tool Abuse in Agentic AI
  7. Unauthorized Access, API Abuse, and Training Data Risks
  8. AI-Generated Misinformation and Emergent Threats

Chapter 4: AI Security Governance Frameworks and Standards

  1. NIST AI Risk Management Framework (AI RMF)
  2. NIST Cybersecurity Framework (CSF) and AI Security
  3. ISO/IEC 42001: AI Management System
  4. ISO/IEC 27001 and AI Security Controls
  5. ISO/IEC 23894 and ISO/IEC 38507
  6. OWASP LLM Top 10 and Generative AI Security
  7. SOC 2 and AI Services
  8. EU AI Act and Regulatory Landscape
  9. Framework Selection and Integration Strategy

Chapter 5: AI Security Architecture

  1. AI Security Architecture Principles
  2. Governance and Organizational Architecture
  3. Data Security Architecture for AI
  4. Model and ML Infrastructure Security
  5. Application and API Security for AI Systems
  6. Identity, Access, and Secrets Management
  7. Network Security, Isolation, and Sandboxing
  8. CI/CD, MLOps, and LLMOps Security
  9. Runtime Protection and Observability
  10. Third-Party and Cloud AI Services

Chapter 6: Securing the AI Lifecycle

  1. Strategy and Requirements Phase Security
  2. Data Acquisition and Preparation Security
  3. Model Development and Training Security
  4. Testing, Validation, and Pre-Deployment Assurance
  5. Deployment and Integration Security
  6. Operational Monitoring and Maintenance
  7. Model Drift, Retraining, and Change Management
  8. Retirement, Disposal, and Decommissioning

Chapter 7: AI Governance Programs and Organizational Models

  1. Establishing an AI Governance Program
  2. AI Security Program Design and Structure
  3. Roles, Responsibilities, and Accountability
  4. AI Governance Committees and Decision Bodies
  5. Policies, Standards, and Operating Procedures
  6. AI Classification Schemes and Risk Tiers
  7. Risk Ownership and Acceptance Processes
  8. Organizational Models: Centralized, Federated, and Hybrid

Chapter 8: AI Risk Management and Control Frameworks

  1. Model Risk Management: Principles and Practice
  2. AI Control Frameworks and Control Objectives
  3. AI and Model Inventories
  4. Risk Registers and Risk Assessment Methods
  5. Security Baselines and Control Matrices
  6. Assurance Criteria and Evaluation Standards
  7. Managing Risk Appetite and Risk Tolerance
  8. Exception Management and Risk Acceptance

Chapter 9: AI Assurance, Testing, and Validation

  1. Principles of AI Assurance
  2. AI Red-Team Programs and Methodology
  3. Penetration Testing for AI Systems
  4. Adversarial Testing and Robustness Evaluation
  5. Model Evaluation and Validation Frameworks
  6. Independent Review and Audit Programs
  7. Bias, Fairness, and Safety Testing
  8. Assurance Evidence and Documentation

Chapter 10: AI Security Operations and Continuous Monitoring

  1. AI Security Operations Centers and Capabilities
  2. Runtime Monitoring and Anomaly Detection
  3. Prompt and Response Logging
  4. Alerting, Detection, and Incident Response
  5. AI Incident Classification and Playbooks
  6. Business Continuity and Resilience for AI Systems
  7. Third-Party AI Service Monitoring

Chapter 11: Integrating AI Security with Enterprise Functions

  1. Integration with Enterprise Cybersecurity
  2. Integration with Privacy and Data Governance
  3. Integration with GRC and Risk Management
  4. Integration with Internal Audit and External Assurance
  5. Integration with Legal and Regulatory Functions
  6. Integration with Procurement and Vendor Management
  7. Integration with Software Development and DevSecOps
  8. Integration with Business Continuity and IT Operations

Chapter 12: Advanced Topics - Agentic AI, Cloud AI, and Emerging Risks

  1. Security Challenges of Agentic and Autonomous AI
  2. Cloud AI Services: Security and Shared Responsibility
  3. Foundation Model Security and Governance
  4. RAG Architecture Security
  5. Multimodal AI Security Considerations
  6. Frontier AI and Advanced Model Risks
  7. Open Source AI Security
  8. Emerging Threats and Future Directions

Chapter 13: Measuring Maturity, Compliance, and Continuous Improvement

  1. AI Security Maturity Models
  2. KPIs, KRIs, and Performance Metrics
  3. Demonstrating Regulatory Compliance
  4. Audit Programs and Evidence Requirements
  5. Traceability and Documentation
  6. Lessons Learned and Continuous Improvement
  7. Board Reporting and Executive Communication
  8. Building an AI Security Culture

Chapter 14: Conclusion - The Future of AI Security Governance

  1. Key Takeaways: Principles That Endure
  2. The Evolving Threat Landscape
  3. Technology Trends and Their Security Implications
  4. The Role of Standards and Regulation
  5. A Call for Integrated, Proactive AI Security
  6. Where to Go from Here

References

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