AI Security and Governance: Technical and Legal Foundations

As artificial intelligence rapidly integrates into modern business, securing these systems requires a unique blend of technical defense and legal compliance. Understanding how to protect AI models while navigating evolving regulations is now a critical skill for both tech and legal professionals. This comprehensive, text-only course guides you from foundational security concepts to practical risk management strategies. You will gain a clear framework for identifying vulnerabilities, ensuring data privacy, and aligning AI deployments with current regulatory standards. What you'll learn: Understand foundational AI security terminology, model architectures, and the unique threat landscape of machine learning; Identify critical vulnerabilities such as prompt injection, data poisoning, and model extraction attacks; Apply modern zero-trust principles and secure development lifecycles to AI pipeline deployments; Navigate legal frameworks, compliance requirements, and intellectual property challenges associated with AI; Formulate risk mitigation strategies that bridge technical engineering controls with corporate governance policies. The course begins with essential definitions and core security principles before moving into technical vulnerability analysis and legal compliance frameworks. You will progress through practical scenarios and written exercises designed to build real-world decision-making skills. This course is designed for beginners, including technical professionals looking to understand AI compliance and legal or risk professionals seeking a foundational grasp of AI security. No prior programming or security experience is required. Start reading today to master the intersection of AI security, law, and risk management.