Predictive Modeling of Insider Threats and Corporate Espionage Through Employee Activity Analysis

Insider threats are a critical challenge to organizational security because they emanate from trusted individuals with authorized, legitimate system access, making them difficult to detect using traditional perimeter-based defenses. These internal risks can cause data breaches, loss of intellectual property, and operational interruptions. The proposed solution is an Insider Threat Detection System that implements machine learning with real-time behavioral monitoring combined with advanced analytics in order to detect high-risk activities before any damage can occur. The objective of the following solution is to provide an intelligent, proactive, behavioral anomaly detection mechanism for organizations that can monitor employee behavior through both supervised models, such as Random Forest for threat classification, and unsupervised models, such as Isolation Forest for anomaly detection. It processes a variety of indicators of behavior, such as logs of access, email interactions, file transfers, and working hours, while assigning dynamic risk scores. Its novelty lies in integrating weighted behavioral analytics, dimensionality reduction by PCA, and a real-time Streamlit dashboard, thus enabling precise identification and prompt incident responses.