Employee cybersecurity risk monitoring flowchart using ML-driven anomaly detection to identify insider threats in real time. The pipeline collects employee activity logs (login, file access, USB transfers, data movement), engineers behavioral features per user per day, and applies Isolation Forest or Autoencoder models to compute a 0-100 risk score. Low and medium risk events are logged for continuous monitoring, while high-risk anomalies trigger immediate alerts to security analysts who review findings, generate audit reports, and escalate for administrative action. This architecture demonstrates a practical insider threat detection workflow that balances automated detection with human review, reducing false positives while catching genuine security incidents. Fork and customize this flowchart on Diagrams.so to adapt feature engineering logic, adjust risk thresholds, or integrate with your SIEM or identity platform.