This rule identifies potential data exfiltration activities by analyzing network traffic patterns using machine learning. It specifically detects unusual data transfers to geo-locations not typically associated with an organization's normal operations. By setting an anomaly threshold at 75, the rule flags significant deviations in data volume that could indicate data exfiltration via command and control channels. The rule is part of a broader Data Exfiltration Detection integration and requires that network and file events be monitored. Investigation steps should include reviewing alert details, analyzing logs, and correlating with other security events to discern legitimate traffic from potential threats. False positives may arise from legitimate business activities such as data transfers for backup or collaboration, and these should be addressed by adjusting detection parameters and updating the model's baseline. The rule aids in early detection by enabling security teams to act promptly upon identifying such anomalies, emphasizing the necessity for ongoing monitoring and refinement of detection methodologies.