Machine Learning-Powered Insider Threat Detection System for Organizational Security and Risk Mitigation

Abstract

Insider threats pose a significant risk to organizational security, as malicious or negligent actions by authorized users can lead to data breaches, financial losses, and operational disruptions. Traditional security mechanisms such as rule-based monitoring and signature-based detection are often ineffective in identifying complex and evolving insider behaviours. This paper presents a Machine LearningPowered Insider Threat Detection System that analyzes user behaviour patterns to detect potential insider threats accurately. The proposed system utilizes machine learning algorithms to evaluate behavioural attributes and classify activities as normal or malicious. A webbased interface is developed using Python, HTML, CSS, and JavaScript to allow easy interaction and real-time threat prediction. Experimental results demonstrate that the system effectively identifies insider threats and enhances organizational security by enabling proactive risk mitigation.