Insider threat detection: Strengthening enterprise IAM (Identity and access management) landscape

Insider threats are one of the biggest threats to the organization's security, given that they often avert detection by standard protection measures because of legitimate access. This research examines IAM systems' ability to alarm and prevent enterprise insider threats. The study's research objectives are as follows: The study will discuss the effectiveness of IAM frameworks and identify the gaps, and the study will equally investigate enhanced detection methods like behavioral analytics and third-generation methods like machine learning, real-time monitoring, and others. Key concepts for the methodology include a survey of the state of IAM, case studies focusing on insider threats, and emerging technology analysis. Such analysis shows that while more conventional IAM systems may be crucial to ensure user access management, these systems need to be revised to help uncover patterns of behavior that point to a malicious user. Notably, there needs to be more literature on the real-time monitoring of IAM systems and the integration of behavior analytics; therefore, the study advances recommendations for improving these systems. The research’s material results prove that there is a potential for various organisations to enhance IAM by implementing modern threat identification techniques for addressing new types of threats. This paper ends with recommendations on how IAM practice and research can improve the constantly updating internal threats.