Insider threat detection: Strengthening enterprise IAM (Identity and access management) landscape
Department of Computer Science, Southern New Hampshire University, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(02), 515-527.
Article DOI: 10.30574/wjaets.2024.13.2.0633
Publication history:
Received on 09 November 2024; revised on 15 December 2024; accepted on 18 December 2024
Abstract:
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.
Keywords:
Insider Threats; Zero Trust; Machine Learning; Blockchain Security; Data Integrity; Anomaly Detection
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0