Enhancing Role-Based Access Control through Artificial Intelligence and Machine Learning

Abstract

In this research paper, an attempt has been made to discuss the implementation of AI & ML to improve the existing RBAC systems. Having used a qualitative research approach, the study evaluates the secondary materials from scholarly articles and industrial white papers found in Google Scholar and ProQuest. The research shows that AI and ML may improve RBAC by dynamically changing responsibilities, identifying dangers, and reducing false alarms. AI enables predictive analysis to avoid security risks and analytical power to oppose and adapt to working situations. AI and RBAC integration improve security and system responsiveness by standardizing the working process. The paper recommends more research on algorithms and ethics, as well as industry-specific solutions. These enhancements should make RBAC more versatile, efficient, and secure for allowing and controlling access levels in varied organizational situations.