About This Architecture

Insider threat detection system using behavioral analytics combines six-stage pipeline from authentication, file access, email, device, application, and network logs through preprocessing and baseline modeling. The architecture extracts behavioral absence patterns and feeds them into ensemble ML models—Random Forest, XGBoost, Support Vector Machine, Isolation Forest, and LSTM—to classify threats as normal, suspicious, insider threat, account compromise, or advanced persistent threat. This approach detects anomalies by learning what users should do, then flagging deviations in login frequency, communication, file access, application usage, and temporal behavior. Security teams gain actionable risk scores, threat reports, real-time alerts, and recommendations to investigate and contain insider threats before damage occurs. Fork this diagram on Diagrams.so to customize detection thresholds, add your own data sources, or integrate with your SIEM platform.

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How do you detect insider threats using behavioral analytics and machine learning?

This diagram shows a six-stage pipeline that ingests organizational logs, preprocesses them, builds user behavioral baselines, extracts absence patterns, applies ensemble ML models (Random Forest, XGBoost, SVM, Isolation Forest, LSTM), and classifies threats as normal, suspicious, insider threat, account compromise, or APT. Risk scores and real-time alerts enable rapid investigation.