This module aims to develop a comprehensive understanding of the principles, legal frameworks, technical measures, and governance models associated with privacy and data protection. It equips students with the analytical and practical skills necessary to assess privacy risks, implement regulatory compliance (particularly under the GDPR), and apply privacy-enhancing techniques in digital environments. The course provides a cross-disciplinary perspective integrating law, policy, and technology, preparing students for professional roles in compliance, security, data management, and ethical system design.

Upon successful completion of this module, students will be able to:

• Understand the core concepts of privacy, the rationale for data protection, and the foundational principles of the GDPR, including lawfulness, fairness, transparency, and data minimization.
• Interpret and compare the GDPR with other international data protection regimes, including compliance obligations and cross-border data transfer mechanisms.
• Analyze organizational models of privacy governance, including roles of controllers, processors, and Data Protection Officers (DPOs), with emphasis on accountability and audit readiness.
• Apply GDPR compliance measures such as consent mechanisms, lawful bases for processing, record-keeping, and breach notification procedures within real-world scenarios.
• Design and critically assess privacy policies and notices that are clear, compliant, and user-friendly, reflecting transparency and data subject rights.
• Conduct risk assessments to identify threats to personal data and propose mitigation strategies aligned with legal, ethical, and operational best practices.
• Understand when DPIAs are required, how to conduct them, and how to interpret findings to ensure data processing respects fundamental rights.
• Integrate privacy principles into system design from inception, applying techniques that ensure personal data is protected by default settings and technical configurations.
• Evaluate the impact of AI systems on data protection, including risks of profiling, bias, and lack of transparency, and identify regulatory and ethical controls.
• Explore key categories of PETs (e.g., anonymization, differential privacy, encryption) and assess their role in reducing data exposure and regulatory risks.
• Design strategies to promote privacy literacy and cultivate a culture of data protection across diverse organizational contexts.
• Understand the function of cyber insurance in mitigating financial consequences of data breaches, and its integration into broader risk management frameworks.
• Analyze how personal data is used in digital advertising ecosystems and evaluate regulatory constraints on tracking technologies, cookies, and profiling under the GDPR and ePrivacy rules.