This rule, authored by Elastic, is designed to identify instances where multiple unique machine learning alerts are triggered for the same influencer field, aiding analysts in prioritizing triage and response efforts. The rule operates by querying alerts data for machine learning jobs, counting distinct job IDs associated with each influencer field while excluding certain system accounts. By flagging cases with three or more distinct job IDs from the same influencer, it highlights potentially malicious activity likely connected to compromised user accounts. The accompanying analysis delves into possible investigation steps, tackling false positives, and outlining response measures for affected user accounts. Analysts should review alert specifics, monitor user activity patterns, cross-check with legitimate actions, and assess the potential for unauthorized access. False positives can stem from benign automated processes or legitimate high-volume users, necessitating role-based exceptions. In case of a confirmed threat, immediate steps include isolating the user account, scrutinizing access logs, removing malware, and notifying relevant teams for further action. Continuous monitoring and adjustments to access controls are advised to prevent future incidents.