The adoption of AI in security systems transforms the way organizations detect threats, monitor activities, and respond to cyber and physical security risks. AI-driven security technologies leverage advanced data analytics, machine learning, and automated decision-making to enhance efficiency and strengthen protection against attacks. However, the collection, processing, and analysis of personal and sensitive information raise legal and privacy concerns. Issues related to data protection, user consent, transparency, accountability, algorithmic bias, and regulatory compliance have become central to the responsible deployment of these systems. As governments and regulatory bodies govern AI and data privacy, understanding the legal and ethical implications of AI-driven security systems may help balance innovation, security, and the protection of individual rights. Legal and Privacy Considerations for AI-Driven Security Systems examines how intelligent and generative models transform cybersecurity practices. It addresses the new threats, vulnerabilities, and ethical concerns introduced by these technologies. This book covers topics such as data science, supply chains, and cybersecurity, and is a useful resource for engineers, government officials, policymakers, security professionals, academicians, researchers, and scientists.