The rule titled 'Unusual City for an Azure Activity Logs Event' is designed to leverage machine learning to analyze Azure Activity Logs and detect any events that originate from a city that is atypical for the action being taken. This anomaly may indicate compromised credentials or unauthorized access by a threat actor operating from a geographic location different from that of the legitimate user. The job utilizes an anomaly threshold of 50 and assesses activities over a 15-minute interval against the last two hours of logged data. Due to the nature of cloud environments, various factors such as manual troubleshooting, changes in automation scripts, or expansions into new regions can contribute to false positives. Therefore, users are cautioned to consider the context of the detected activity. In the event of activation issues with the associated machine learning job, troubleshooting steps are provided to ensure proper configuration. The rule is part of a production maturity phase and requires specific setup procedures for both the machine learning jobs and Azure Activity Logs integration to be effective. This rule contributes to enhancing security posture by identifying potentially suspicious activities that otherwise may have been overlooked.