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

Nikhil Ghadge *

Software Architect Okta.Inc, Software Engineering, Dublin, CA, USA.
 
Review
International Journal of Science and Research Archive, 2024, 11(02), 2050–2057.
Article DOI: 10.30574/ijsra.2024.11.2.0761
 
Publication history: 
Received on 16 March 2024; revised on 27 April 2024; accepted on 29 April 2024
 
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.
 
Keywords: 
Identity and Access Management; Security; Threat detection; Identity theft; Artificial Intelligence; Machine Learning
 
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Copyright information: 

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