This book provides a comprehensive guide to integrating Artificial Intelligence (AI), Large Language Models (LLMs), and quantum-safe security mechanisms into modern cybersecurity practices. It begins by examining the evolving cyber threat landscape and the growing need for intelligent, adaptive defense mechanisms. Also, the role of AI, LLMs, and quantum computing in cybersecurity, outlining the book s objectives and structure are introduced. The fundamentals of LLM architectures and training methodologies are explored, followed by their practical applications in threat detection, malware analysis, phishing prevention, and automated incident response.
Practical integration of LLMs into cybersecurity frameworks is discussed, including enhancements to intrusion detection systems, security information, Event Management (SIEM) platforms, and real-time threat intelligence. Real-world case studies highlight successful deployments. This book also addresses key challenges such as interpretability, scalability, ethical concerns, bias, and adversarial vulnerabilities, offering mitigation strategies and best practices. The quantum computing section examines threats to classical cryptography and presents quantum-safe solutions, including post-quantum cryptography and Quantum Key Distribution (QKD). The convergence of AI and quantum-safe security is explored, showing how LLMs can support quantum-resilient defenses.
This book is designed for cybersecurity professionals, researchers, and policymakers seeking effective strategies to secure increasingly complex digital environments. Advanced-level students majoring in computer science and/or ML/DL engineering will also find this book useful as a reference.
