The 'Rare Azure Activity Logs Event Failures' detection rule leverages machine learning to identify unusual failure events within Azure Activity Logs, which may signal potential security threats such as persistence attempts, privilege escalation, defense evasion, discovery aspects, lateral movement, or data collection. The rule is specified for a 15-minute interval and looks back over the last two hours for anomalies. It has a low severity but a risk score of 21, indicating noteworthy activity that requires investigation. The rule's setup mandates the integration of specific Azure Activity Logs and the initialization of related machine learning jobs. It outlines the necessity of proper configurations to ensure the machine learning job runs effectively, and it can produce false positives in situations where failures are atypical, possibly arising from manual troubleshooting or automation errors. The rule is suitable for environments leveraging Azure cloud services and is designed to enhance visibility over user and service behavior during critical operations. Its integration with the MITRE ATT&CK framework allows it to classify detected anomalies under tactics such as Discovery, Privilege Escalation, Defense Evasion, Lateral Movement, Persistence, and Collection, providing a structured approach to threat analysis and response.