This detection rule, authored by Elastic, employs machine learning to identify instances where a user performs privileged operations in Windows from an uncommon geographic location. Such behavior may indicate compromised accounts, unauthorized access, or the use of stolen credentials to perform privilege escalation. The analysis leverages Elastic's anomaly detection functionalities and is designed to trigger on events deemed anomalous based on historical user behavior. The rule operates on data collected from Privileged Access Detection (PAD) integration assets and Windows logs. It employs a threshold of 75 for anomalies and generates alerts for potentially risky operations that deviate from typical login patterns. Investigations prompted by this detection should focus on evaluating the legitimacy of geographic access alongside reviewing the user’s account activity. False positives may arise from legitimate business travel or remote work, necessitating the development of allowances for known patterns to fine-tune the detection process. The presence of this rule is crucial for organizations striving to safeguard against unauthorized escalation of privileges.