Impact of DSPM on Insider Threat Detection: Exploring how DSPM can enhance the detection and prevention of insider threats by monitoring data access patterns and flagging anomalous behavior

Himanshu Sharma

Authors

Himanshu Sharma Principal Engineer, Netskope Inc, Santa clara, USA Author

Keywords:

Data Security Posture Management (DSPM), insider threats, data access patterns, anomaly detection, machine learning, cybersecurity, data protection, threat detection

Abstract

In the modern digital landscape, safeguarding sensitive data against insider threats is a critical challenge for organizations. Data Security Posture Management (DSPM) has emerged as an advanced approach to enhancing data security by providing comprehensive monitoring and management of data access and usage. This paper explores the impact of DSPM on insider threat detection, focusing on how DSPM can improve the identification of anomalous behavior and prevent potential security breaches. Through a detailed review of real-world implementations and case studies from organizations such as Capital One, Sony, and Netflix, the effectiveness of DSPM in monitoring data access patterns and flagging anomalies is demonstrated. The paper also addresses the limitations of DSPM, including issues with false positives and integration challenges, and proposes future research directions to refine DSPM methodologies. By integrating advanced analytics and machine learning, DSPM offers a proactive framework for defending against insider threats, ensuring enhanced data protection and organizational security.

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