Ensuring AI Data Access Control in RDBMS: A Comprehensive Review

William Kandolo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 8400-8407

This paper presents a comprehensive review of current methodologies for ensuring data access control in relational database management systems (RDBMS) within the context of artificial intelligence (AI) applications. As AI systems increasingly rely on large volumes of data stored in RDBMS safeguarding data privacy and integrity through robust access control mechanisms has become critical. We evaluate various techniques including role-based access control (RBAC) attribute-based access control (ABAC) and emerging AI-driven access control models. Our analysis identifies strengths and limitations offering insights into future research directions and best practices for implementing effective data access control in AI-driven environments.