Traditional database management systems rely heavily on perimeter-based security models that implicitly assume trust within organizational boundaries. This conventional approach often leaves databases vulnerable to insider threats, credential misuse, and dynamic cyberattacks that exploit static trust assumptions. To overcome these challenges, the Zero Trust paradigm introduces a “never trust, always verify” philosophy, ensuring that every request, user, and process undergoes continuous verification before gaining access to critical data assets. This research explores an AI-driven Zero Trust architecture tailored for privacy-centric database management systems. The integration of Artificial Intelligence enables adaptive trust management, where access decisions are dynamically adjusted based on behavioral patterns, contextual risk, and anomaly detection. The proposed framework incorporates continuous authentication, predictive analytics, and privacy-preserving mechanisms such as encrypted data transactions and intelligent policy enforcement. Experimental evaluations demonstrate improved data confidentiality, reduced attack surfaces, and enhanced decision precision compared to conventional access control methods. The study concludes that AI-augmented Zero Trust architectures represent a promising pathway toward self-defending, privacy-oriented, and resilient next-generation database ecosystems.

Keywords: Artificial Intelligence, Zero Trust Architecture, Database Security, Privacy Preservation, Adaptive Access Control, Continuous Authentication, Trust Evaluation