Data Privacy and Security

In an era where Artificial Intelligence (AI) is proliferating every system with processes that rely heavily on large amounts of personal and sensitive data, data privacy and security have become a central concern for ethical AI governance. This chapter explores this evolving landscape, particularly the AI-driven data processing within the rapidly evolving digital and regulatory landscapes of the Sub-Saharan African context. The chapter highlights the peculiarities surrounding AI-system privacy and security challenges. Drawing on the contextual integrity, social control, information boundary, and ethical decision-making theories, this chapter explores how Privacy-Enhancing Technologies (PETs) and governance approaches can reduce risks arising from the vast data processing and algorithm decision-making. Employing a qualitative integrative review approach that synthesizes recent scholarly work, policy frameworks, and regional initiatives, the chapter identifies and addresses the existing challenges faced with balancing data-driven innovation with robust privacy protections and suggests actionable roadmaps for integrating privacy and security principles into AI system development. The chapter synthesis also highlights recurrent issues like fragmented regulatory frameworks, limited infrastructural facilities, resource constraints, and gaps in capacity building, underscoring opportunities for responsible AI adoption through context-specific governance and technical safeguards that use PETs such as differential privacy, end-to-end encryption, federated learning, privacy-by-design approaches, and homomorphic encryption that enhances security while maintaining AI’s functionality. Therefore, ensuring AI systems remain accountable, transparent, and aligned with user rights. Through this integrative approach, the chapter reinforces the need for a multidisciplinary strategy combining law, technology, and policy with designing AI ecosystems that safeguard privacy while enabling responsible technological advancements. The chapter, therefore, concludes with practical recommendations for integrating AI innovations with strong data governance and ethical accountability for the Sub-Saharan African context.