AI Security Engineering: Red Team & Blue Team Study Notes
This book is designed for AI engineers, cyber security professionals, SOC analysts, AI Red Teamers, penetration testers, machine learning practitioners, malware researchers, and security architects seeking a modern understanding of AI Security in real-world environments.
Rather than focusing on abstract policy discussions or compliance-driven theory, the book delivers an operational and engineering-focused methodology for building resilient AI systems capable of surviving hostile environments.
Across its chapters, readers will learn how modern AI systems fail under adversarial pressure, why traditional accuracy metrics create dangerous illusions of safety, and how attackers weaponize gradients, prompt structures, semantic manipulation, and token-level behavior to compromise models.
The book systematically explores AI guardrails, adversarial machine learning, LLM defense strategies, prompt injection mitigation, semantic validation pipelines, AI-based content moderation, and defense-in-depth architectures specifically designed for modern generative AI ecosystems.
The material also dives deeply into AI Red Teaming methodologies, demonstrating how offensive techniques expose weaknesses in model alignment, inference pipelines, and deployment architecture.
Readers will examine real adversarial workflows including jailbreak engineering, token manipulation, malicious prompt chaining, inference abuse, and gradient-based attacks such as FGSM and I-FGSM.
These concepts are paired with practical defensive implementations including adversarial training, epsilon spread training, model hardening, sanitization layers, semantic filtering, and AI-driven validation systems.
Unlike beginner-level AI books that remain theoretical, this handbook emphasizes implementation. Readers will build practical guardrails using Python, Pydantic, Guardrails AI, semantic validators, and enterprise-grade filtering pipelines.
The book also explores external AI defense services, scalable AI security architectures, detection engineering strategies, and methods for securing AI-enabled applications operating in production environments.
This resource additionally serves as a strong technical foundation for professionals preparing for AI Security certifications, AI governance programs, AI risk management roles, secure AI engineering positions, and advanced AI Red Teaming career tracks. As organizations increasingly demand professionals capable of securing generative AI systems against adversarial abuse, the skills covered in this book become critical across enterprise, government, and offensive security sectors.
Whether your goal is defending enterprise LLM deployments, performing AI penetration testing, building secure AI applications, conducting adversarial research, or preparing for the next generation of AI Security certifications, this book delivers the practical engineering knowledge required to operate in the evolving battlefield of artificial intelligence security.
Table of Contents
- Introduction to AI Security
- Building AI Models
- Understanding How LLMs Actually Work
- AI Security Frameworks
- AI Threat Modeling
- AI Security in Depth
- AI Red Teaming
Page Count: 202
Format: PDF
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