Build a User Behavior Analytics System for Insider Threat Detection

Not all cybersecurity threats come from the outside — employees and contractors with authorized access can pose significant risks. Monitoring user behavior helps detect policy violations, excessive privilege use, or unusual activity before it leads to data breaches or sabotage.

This system collects user activity logs across various systems and uses rule-based or statistical models to detect anomalies. It builds a baseline of typical user behavior and flags deviations that may indicate risky behavior or malicious intent.

The tool aggregates logs from multiple data sources (e.g., authentication systems, endpoint logs), builds baseline behavior per user, and constantly compares new activities against these baselines. When it detects a significant deviation — such as a login from an unusual location or excessive file transfers — it triggers alerts.

• Collect data from log files, endpoint agents, or server APIs.
• Normalize and enrich logs with contextual data (e.g., geolocation, user role).
• Profile users based on login time, access frequency, tools used, etc.
• Flag deviations using statistical thresholds, clustering, or rule-based scoring.
• Generate reports and alerts with timelines and behavioral risk scores.

• Insider Threat Mitigation Guide – CISA
• UEBA Explained – Gartner
• Anomaly Detection in Python – DataCamp
• Scikit-learn Anomaly Detection
• MITRE ATT&CK Insider Threat Patterns