A Digital Approach to Identifying Insider Threats in Higher Education Institutions

Abstract

Insider threats pose significant risks to higher education institutions (HEIs), where sensitive data through the Personally Identifiable Information (PII), intellectual property, and student information are prime targets. This paper proposes a digital approach to identifying insider threats by leveraging machine learning, behavioral analytics, and network monitoring. We present a framework that integrates user behavior profiling, anomaly detection, and real-time monitoring to detect potential malicious activities. Through a case study at a large university, we demonstrate the effectiveness of our approach in identifying suspicious behaviors with a detection accuracy of 92%. The results highlight the potential of data-driven methods to enhance institutional security while addressing challenges such as privacy concerns and false positives. This work provides a scalable model for higher education institutions to mitigate insider threats effectively.