Enhancing threat detection in Identity and Access Management (IAM) systems

Abstract

Identity and Access Management (IAM) systems play a pivotal role in safeguarding organizational resources by controlling access to sensitive information. However, these systems face evolving threats that can compromise security and privacy. This paper proposes a comprehensive approach to enhance threat detection within IAM systems. By integrating advanced techniques such as anomaly detection, machine learning, and behavior analysis, organizations can better identify and respond to suspicious activities. This paper discusses the challenges associated with threat detection in IAM systems and presents practical solutions to mitigate these risks. Furthermore, the paper highlights the importance of continuous monitoring and adaptation effectively combat emerging threats.