Responsible AI in database systems: Governance frameworks for generative AI data access

This article introduces a novel governance framework addressing the unique challenges of managing generative AI data within database systems. While extensive literature examines responsible AI principles in theory, a significant gap exists in translating these ethical frameworks into practical implementation at the database layer. The article presents a comprehensive approach that bridges this divide through a layered architecture incorporating fine-grained access controls, comprehensive lineage tracking, and automated policy enforcement mechanisms specifically designed for generative AI workloads. The article addresses distinctive challenges, including complex data transformations, synthetic content generation, purpose limitation in repurposed data, and evolving consent requirements that traditional governance models fail to adequately manage. The article demonstrates substantial improvements in governance effectiveness compared to conventional approaches. This article provides database administrators and AI practitioners with concrete strategies for maintaining ethical boundaries throughout the data lifecycle while enabling responsible innovation. The framework establishes a foundation for operationalizing AI ethics at the infrastructure level, ensuring that governance considerations become integral to system design rather than retrospective considerations