Behavior Threats supports LLM powered user risk summary of the top 0.1% of risky users. This summary provides detailed insights into unusual activities, data access patterns, and potential security concerns even when incidents are not generated, enabling security administrators like you to understand and assess user risk more effectively. LLM-powered user risk summary is an innovative approach for evaluating high-risk users by analyzing their activity patterns and machine learning model results. This summary offers an overview of user risk factors, surpassing the limitations of current incident descriptions that often focus on single aspects. It's valuable for explaining high risk scores for users without recorded incidents. This approach has shown promising results in production, offering additional insights compared to traditional incident descriptions.