Data Security in AI Systems: Strategy for Data Protection and Privacy

This book explores the vital intersection of AI and data security, stressing the need to safeguard sensitive information in intelligent systems. Part 1 introduces AI fundamentals and highlights why data security is critical as AI increasingly relies on large-scale data processing, making it vulnerable to threats. Part 2 outlines major risks like data poisoning, adversarial attacks, and model theft, along with protection methods such as encryption and differential privacy. Part 3 presents a case study on Google, examining how it manages data security and addresses real incidents. Part 4 explores future challenges, including quantum computing, evolving regulations, and the push for explainable AI. Part 5 concludes with practical recommendations to enhance AI security. Bridging technical detail and real-world examples, the book is a valuable resource for researchers, developers, and policymakers seeking to strengthen AI systems in a data-driven world.