This detection rule identifies and correlates alerts from diverse integrations that involve varying `user.name` values which might represent the same real-world identity. By employing a large language model (LLM) for semantic similarity analysis, the rule assesses multiple user identifiers—including their variations, formats, aliases, or domain discrepancies—to determine if they likely belong to the same individual. The underlying query collects high-risk alerts within a defined timeframe (last 60 minutes), filtrating out standard user IDs and system-generated alerts to focus on high-severity incidents. After accumulating and grouping alerts based on time and distinct values, the rule prepares a prompt for the LLM that requests a similarity analysis of the identified user names. The final output helps determine if the correlated users might be the same person, which could indicate potential account compromise or identity misuse. Analysts are provided with guidance for investigating correlated alerts, responding to potential compromises, and understanding false positives, thus enhancing overall threat detection and response efficiency.