Generative AI Security Engineering: A Practical Guide to Securing LLM Applications, Protecting AI Systems and Defending Against Cyber Threats

Generative AI Security Engineering: A Practical Guide to Securing LLM Applications, Protecting AI Systems and Defending Against Cyber Threats provides a clear, practical approach to understanding and addressing the security risks of modern AI applications.

Written in straightforward American English, this book explains complex security concepts without overwhelming readers with difficult terminology. It takes you from the foundations of generative AI security to the essential practices used to develop, test, deploy, and maintain more secure AI systems.

WHAT YOU WILL LEARN INSIDE THIS BOOK

• Understand Generative AI Security: Discover how large language models work, where security weaknesses appear, and how attackers exploit vulnerable AI applications.
• Prevent Prompt Injection and Jailbreak Attacks: Learn how malicious instructions manipulate AI behavior and explore practical defenses that reduce these risks.
• Secure LLM Applications: Understand how to protect system prompts, validate user inputs, check generated outputs, and secure connections to external services.
• Protect Sensitive Data: Learn how to prevent information disclosure, protect user records, apply encryption, and handle confidential data safely.
• Secure AI Agents and Automated Workflows: Understand how to control tool permissions, prevent unauthorized actions, and protect AI systems that perform tasks automatically.
• Protect RAG Applications: Learn how to secure document retrieval, vector databases, and external knowledge sources against unauthorized access and malicious content.
• Strengthen Authentication and Access Control: Discover how to manage user identities, secure API credentials, and apply role-based permissions and least privilege.
• Defend AI Models and Software Dependencies: Understand model theft, malicious model files, supply chain attacks, and secure deployment practices.
• Test AI Systems for Security Weaknesses: Explore threat modeling, AI red teaming, security assessments, and tools used to identify vulnerabilities.
• Build Secure Production AI Systems: Learn how to apply runtime protection, security monitoring, incident response, and ongoing risk management.

This guide connects important security concepts with realistic scenarios, practical defensive measures, and clear explanations. It also includes useful reference materials, a generative AI security checklist, recommended tools, and a guide to common threats and their defenses. You will learn how to recognize security weaknesses, understand their possible consequences, and select appropriate controls to protect AI applications. You do not need to be an expert in generative AI to follow this guide. Basic knowledge of software applications and cybersecurity will be helpful, but important concepts are explained in clear, accessible language. As generative AI becomes more closely connected to business data, software tools, and automated decisions, security can no longer be treated as an afterthought.

Whether you are developing your first LLM application, protecting an existing AI system, or building your skills in AI cybersecurity, this book will help you understand the risks and apply stronger security practices. Do not wait for a security breach to discover the weaknesses in your AI system.

Take the next step toward understanding prompt injection defense, secure AI architecture, data protection, and practical AI security engineering. Get your copy of Generative AI Security Engineering today and start building, testing, and protecting AI systems with greater knowledge and confidence.