Abstract

In the evolving landscape of data security, traditional access control mechanisms often fall short in addressing dynamic and context-specific requirements. As data breaches become more sophisticated, organizations require more adaptive and intelligent access control strategies. This paper explores the integration of Artificial Intelligence (AI) into Just-in-Time (JIT) access control models to enhance database security. By leveraging AI, we aim to create adaptive, context-aware permission systems that grant access precisely when needed, reducing the attack surface and mitigating unauthorized access risks. We begin by analyzing traditional access control methods such as Role-Based Access Control (RBAC), Mandatory Access Control (MAC), and Discretionary Access Control (DAC), highlighting their limitations in handling real-time access control scenarios. The paper then explores the benefits of JIT access control, emphasizing how it minimizes over-privileged accounts and reduces security vulnerabilities. Furthermore, we investigate the role of AI in cybersecurity, particularly in real-time monitoring, anomaly detection, and decision-making processes for access control. To achieve a more intelligent access control framework, we propose an AI-driven JIT access model that incorporates machine learning algorithms, user behavior analytics, and contextual evaluation to determine access permissions dynamically. The proposed model is evaluated through simulations and case studies to measure its effectiveness in preventing unauthorized access, reducing the attack surface, and enhancing overall database security. The results demonstrate that our AI-driven approach significantly improves access accuracy, minimizes false positives and negatives, and optimizes response times compared to traditional methods. Through comprehensive analysis, this research provides a roadmap for organizations looking to implement AIenhanced JIT access control mechanisms. By dynamically granting access based on real-time behavioral and contextual assessments, organizations can significantly improve database security, reduce administrative overhead, and mitigate insider threats. Future research directions include further refinement of AI models, integration with multi-factor authentication mechanisms, and testing across diverse real-world scenarios to enhance security effectiveness.