As digital systems become increasingly integral to daily operations, balancing security and usability in database access control has emerged as a critical concern. This research paper introduces a novel approach that leverages Artificial Intelligence (AI) to dynamically adapt and personalize database access controls based on user behavior and contextual factors. The primary objective is to enhance security while maintaining a seamless user experience. The proposed methodology involves the integration of AI techniques, including machine learning algorithms and behavioral analysis, to continuously assess risk and user interactions. Through data collection and preprocessing, an AI model is trained to recognize patterns in user access, learn contextual cues, and predict potential security breaches. The adaptive access control algorithm, driven by the AI model, dynamically adjusts access permissions in real-time, ensuring that users only access the resources they need, while unauthorized or anomalous activities trigger immediate responses. Experimental evaluations showcase the system's ability to effectively mitigate security risks while providing a user-centric environment. The results highlight improved security levels without sacrificing system usability. This paper contributes a comprehensive framework for personalized database access control, addressing the limitations of traditional static methods and paving the way for a more secure and user-friendly digital ecosystem.

#