Case Studies in Data Protection and Privacy s an advanced course that provides a comprehensive and practice-oriented understanding of how data protection and privacy principles apply to modern technologies, specifically, AI system. As AI systems increasingly shape decisions in industry, government, and society, safeguarding data throughout the AI lifecycle has become an essential professional competency. 

In this course, you will explore the foundations of data protection and privacy with a strong emphasis on their application to AI models, pipelines, and deployments. You will examine how sensitive data is collected, processed, transformed, learned from, and stored in AI-driven systems, and you will investigate emerging risks such as model inversion, membership inference, data leakage, and privacy violations caused by machine learning. 

Other components of the course will also cover advanced technical strategies for privacy-preserving AI. You will engage with contemporary techniques—including federated learning, secure multi-party computation, secure aggregation, and machine unlearning—that enable AI development while reducing privacy risks. You will also analyse the privacy implications of generative AI, foundation models, and automated decision-making systems. 

Through an extensive set of real-world case studies, ranging from AI-related data breaches to failures in algorithmic transparency and privacy governance, you will critically analyse the consequences of inadequate safeguards and the challenges of implementing privacy-preserving AI in practice. These case studies ground the course in contemporary industry realities across sectors such as health, finance, cybersecurity, government, and social platforms.