Description LLMs and GenAI are paving the way for almost all modern applications and transforming all organizations by providing automation, natural language processing, and tailored user experiences. However, these rapid technological advancements come with a unique set of security, privacy, and ethical challenges that require all parties involved to take appropriate measures. This book will offer a comprehensive guide to security of AI-integrated applications, providing readers with knowledge of the principles of AI application security with practical and actionable insights, and enabling them to identify threats, understand risks, utilize different testing methods, and integrate security best practices at each stage throughout the software development lifecycle. By the end of this book, the readers will have a holistic perspective of all the critical areas of AI-based applications security. This deep understanding will help readers make secure choices for AI developments/integrations, assess vulnerabilities, develop mitigation techniques, and incorporate privacy and ethical AI practices. The readers can create safe, compliant, and responsible AI applications. What you will learn Foundational understanding of securing LLM and GenAI developments. Data security and privacy, compliance, and ethical considerations. Advanced security controls, monitoring, incident management, and evaluation techniques. Security of deployment environments and integration with other organizations infrastructure. Applying Zero Trust to LLM architectures. Automating incident response and anomaly detection. Who this book is for This book is intended for security professionals, scholars, software developers/architects/managers, IT leaders, or anyone interested in technological advancements. It will help folks involved in developing and safeguarding GenAI/LLM infrastructures and integrating AI/GenAI/LLMs into their applications. Readers are expected to understand application development, GenAI/LLMs use cases, and fundamental security concepts. Table of Contents 1. Foundations of Application Security and AI Systems 2. Building Secure Framework 3. Authentication, Authorization, and API Security 4. Defending I/O Validation and Filtering 5. Protecting Data Integrity and Privacy 6. Verifying Model and Data Integrity 7. Ensuring Fairness, Transparency, and Accountability 8. Evaluating Testing, Penetration, and Red Teaming 9. Continuous Monitoring and Incident Response 10. Securing AI Integration 11. Scaling AI Security 12. Navigating Compliance and Regulatory 13. Future of Secure AI
