ML-Based Privileged Account Anomaly Detection (Multi-Layer, Self-Calibrating)
Multi-layered detection rule that identifies anomalous behavior on privileged accounts across six independent detection layers: absolute rule violations ("never actions"), self-calibrating ratio-based thresholds, behavioral technique substitution (e.g., WMI drop-off while other lateral movement continues), time-series statistical anomaly detection via series_decompose_anomalies(), reconnaissance burst detection on LDAP/directory queries, and MFA gap analysis. All layers roll up into a composite risk score for analyst prioritization. Originally derived from diffpatterns() analysis of real penetration test data vs. normal operations, this community edition replaces environment-specific hardcoded thresholds with self-calibrating statistical baselines. Requires Microsoft Defender for Identity and Entra ID Sign-in Logs.
Microsoft Sentinel (KQL)
