The rule titled 'Unusual Privilege Type assigned to a User' leverages machine learning to identify potential abuse of privileges by analyzing deviations from established privilege usage baselines. The detection evaluates user activities over the past hour, monitoring for instances where higher-than-usual privileges, especially those not typically employed by the user, are utilized. Authoring by Elastic, this rule executes a machine learning job designed to identify uncommon privilege types linked to a user's operations, which could indicate privilege escalation attempts or other malicious activities. The defined threshold for anomaly detection is set at 75, initiating alerts at a severity level of low. The rule is integrated within the broader Privileged Access Detection framework requiring users to have impacted Windows logs properly collected through integrations such as Elastic Defend and the Windows integration. Detailed investigation steps and false positive avoidance strategies are outlined, ensuring that users can adequately assess anomalies while minimizing unnecessary alerts triggered by legitimate privilege usage or temporary access needs. Potential responses to identified threats include isolating suspect accounts and revoking excessive privileges, emphasizing the critical nature of proactive privilege management in maintaining security across IT environments.