AI is transforming enterprise technology-but are your systems prepared for the security threats that come with it?
From prompt injection and data poisoning to model vulnerabilities, insecure RAG architectures, and autonomous AI agents, this book explores the security challenges that every modern organization must understand.
Mastering AI Security Engineering is a practical guide to designing, securing, testing, and governing enterprise AI systems.
Inside, you will learn how to:
Identify AI-specific threats, attack surfaces, and vulnerabilities.
Secure machine learning models, training data, and AI supply chains.
Defend LLM applications against prompt injection and information leakage.
Build secure Retrieval-Augmented Generation (RAG) architectures.
Control AI agents, tool permissions, and autonomous workflows.
Protect AI applications, APIs, cloud infrastructure, and Kubernetes environments.
Conduct AI security assessments, penetration testing, and red-team exercises.
Implement runtime monitoring, incident response, and enterprise governance.
Measure AI security maturity and build a sustainable security program.
Go beyond understanding AI risks. Learn how to engineer practical defenses.
Whether you are a cybersecurity professional, security architect, AI engineer, technology leader, or enterprise decision-maker, this book provides a structured foundation for building more resilient and trustworthy AI systems.
Build secure AI. Protect enterprise intelligence. Engineer trust.
