Built on Fortune 500 experience, this guide delivers hands-on methods to secure generative AI with extensive coverage of RAG, agents, prompt injection, data pipelines, Zero Trust, and sustainable programs Key Features Clearly identify and manage real-world risks unique to generative AI, confidently explaining their implications to both technical teams and business stakeholders while understanding the complete AI security ecosystem. Book DescriptionContrary to general AI texts or cybersecurity books with limited AI coverage, this guide offers a comprehensive dive into securing the generative AI ecosystem. It moves through five parts: Foundations explains why AI security is unique, covering threat modeling, attack surfaces, and defense principles. Attacks examines vectors against system anatomy, data/models, prompt injection, memory, RAG, and agents, concluding with red teaming and evaluation. Designing, Deploying, and Architecting Secure AI Systems covers secure infrastructure/MLOps, APIs, defensive prompting, agent security, supply chain integrity, and Zero Trust patterns. Operationalizing AI Security and Responsibility addresses governance, risk, compliance (GRC), security operations, safety/alignment, and AI-driven misinformation. Building Sustainable AI Security Programs focuses on organizational capability, threat intelligence, collaboration, and the future of AI security. Throughout, you will gain access to practical insights and structured approaches applicable to real-world scenarios. By the end, you will be able to design, implement, and maintain security programs for generative AI, defend against advanced threats, communicate risks to stakeholders, and establish governance ensuring secure, compliant operations across the lifecycle.What you will learn Identify AI-specific risks and clearly communicate them to business teams Defend models, data, RAG, and agents from threats like poisoning, prompt injection, jailbreaking, and data exfiltration Design resilient cloud/MLOps with Zero Trust, supply chain security, and isolation Build secure APIs, apps, and agents with strong auth, validation, and safe tool use Apply AI-focused GRC, alignment checks, bias mitigation, monitoring, and incident response Translate complex concepts into actionable steps, using threat intel and collaboration for lasting security Who this book is forThis book is for mid- to senior-level cybersecurity professionals, security architects, and tech leaders managing risks in generative AI deployments. It’s also valuable for early-career practitioners, AI/ML engineers, red teamers, DevSecOps, governance specialists, compliance officers, and product stakeholders with foundational cybersecurity knowledge. Readers should have basic familiarity with security concepts, some exposure to cloud platforms (AWS, Azure, or GCP), and a fundamental grasp of AI/ML, though no prior AI security expertise is required.

Table of Contents:
Table of Contents

1. Why AI Security Is Different
2. Threat Modeling AI Systems
3. The AI Attack Surface
4. Foundations of AI Defense
5. Anatomy of an AI System
6. Prompt Injection and Jailbreaking
7. Memory, Context, and State Abuse
8. Attacks on RAG Systems
9. Agent Architecture and Vulnerabilities
10. Agent Exploitation Techniques
11. Attacks on Training Data and Model Integrity
12. AI Red Teaming and Evaluation
13. Securing AI Infrastructure, MLOps, and Runtime Environments
14. Building Secure AI Applications and APIs
15. Defensive Prompt Engineering
16. Guardrails and Human Oversight
17. External Dependencies, Supply Chain Integrity & Multi-Tenant Risk
18. Securing AI Agents with Zero Trust Architecture
19. AI Governance, Risk, and Compliance
20. AI Security Engineering
21. AI Security Operations
22. Building Sustainable AI Security Programs
23. The Future of AI Security