In recent years, enterprises have increasingly relied on artificial intelligence for managing vast and complex databases, yet traditional machine learning models often operate as opaque systems with limited interpretability. This lack of transparency poses significant challenges for ensuring data security, policy compliance, and ethical governance. To address these limitations, Causal Artificial Intelligence (Causal AI) has emerged as a transformative paradigm capable of uncovering cause–effect relationships within data, offering both reasoning capability and interpretive clarity. This review paper explores the role of Causal AI in achieving secure and transparent data governance across enterprise databases. It critically examines how causal inference models enhance explainability, accountability, and decision traceability—key pillars of responsible data management. A systematic review methodology was adopted, encompassing contemporary studies from leading scientific databases published between 2016 and 2025. The analysis categorizes existing frameworks according to their governance objectives, technical depth, and applicability within enterprise systems. The findings reveal that while Causal AI substantially improves trust and compliance mechanisms, challenges remain in scalability, real-time deployment, and integration with existing governance infrastructures. The paper concludes that embedding causal reasoning within enterprise data ecosystems can transform governance models from reactive oversight to proactive, transparent control, thereby laying the foundation for next-generation frameworks that balance innovation, accountability, and security in data-driven organizations.

Keywords: Causal Artificial Intelligence, Data Governance, Enterprise Databases, Explainable AI, Data Security, Transparency, Trust and Accountability.