This detection rule utilizes a machine learning job to identify significant spikes in failed authentication events, indicating potential malicious activities such as password spraying, brute force attacks, or user enumeration. The rule stipulates an anomaly threshold of 75, meaning that if the anomaly score exceeds this value, the system flags it for analysis. The detected spikes could signal an impending account takeover or unauthorized access attempts, making this rule crucial for proactive security measures. False positives can stem from misconfigured service accounts, legitimate password changes, or scheduled security tests including brute force attempts. The configuration requires integrating with systems such as Elastic Defend, Auditd Manager, or System to capture necessary log events for thorough analysis. Detailed setup instructions guide the user through necessary integration steps to ensure data collection accuracy and effective anomaly detection. In case of a triggered alert, an investigative approach is recommended, involving the identification of affected users, checking authentication sources, and reviewing related alerts. If a true positive is confirmed, incident response measures should be initiated to mitigate potential credential exposure and unauthorized access, utilizing the findings to enhance security posture over time.