The Azure Impossible Travels Sign-in rule detects anomalous user sign-in activity by analyzing sign-ins from multiple geographical locations within implausible timeframes. This is achieved by gathering sign-in activity data from Azure, processing it to derive the locations and distances between consecutive logins, and calculating the speed of travel between them. If the identified distance exceeds a threshold (e.g., over 1000 miles) while also observing a time difference that would make such travel impossible, a potential security threat is flagged. This detection logic is particularly useful for identifying compromised accounts where an attacker may be attempting to access an account from various places far apart in a short span of time. The integration of IP geolocation further enhances its effectiveness by providing context around where the sign-ins originated. The data is sorted and stats are collected to provide significant details on the user, distance traveled, speed, and time difference between sign-ins, ultimately alerting on behavior consistent with account compromise or similar threats.