Leveraging Artificial Intelligence for Detecting Insider Threats in Corporate Networks

Abstract

In the modern corporate environment, insider threats pose a significant risk to data integrity, financial stability,
and overall cybersecurity. Unlike external attacks, insider threats originate from individuals within an
organization like employees, contractors, or partners who possess legitimate access to critical systems.
Traditional security measures often fail to identify these threats due to the complexity of distinguishing malicious
behaviour from regular activities. Artificial Intelligence (AI) based systems, with their ability to analyse large
datasets, detect subtle patterns, and adapt to evolving threat landscapes, offer a powerful approach to insider
threat detection. This research involves the application of machine learning algorithms to identify deviations from
normal users’ activities in corporate networks. The methodology involves analysing user behaviours and access
patterns, development and training a machine learning model for classifying user behaviours into normal or
abnormal activity. The system helps to identify abnormal user activities and flags suspicious activities in real time,
providing an early warning sign for potential breaches. The results demonstrate the effectiveness of machine
learning in enhancing threat detection, reducing insider threats, and improving overall cybersecurity in corporate
networks.