Behavioral Threat Detection by NeuralTrust monitors usage patterns in real time to identify abnormal, risky, or compromised interactions with generative AI applications before they escalate into security incidents. The product continuously analyzes how users interact with LLMs to catch patterns that slip past prompt-based filters. The system tracks session evolution across multiple messages through multi-turn analysis to catch complex exploit chains. It assigns dynamic threat scores to sessions based on historical and real-time data through anomaly scoring. The product identifies behavioral outliers such as sudden prompt flooding, input structure changes, or tone shifts through deviation detection capabilities. The solution can automatically block users, IPs, or sessions that exceed configurable threat thresholds. It detects coordinated attacks, abuse, or usage anomalies across thousands of users at scale. The product integrates with existing security stacks and workflows, pushing alerts and session data into SIEM tools for centralized security visibility. The system pairs with moderation policy engines to enforce conditional rules based on threat behavior. It provides granular observability to trace every interaction, policy trigger, and block decision for forensic analysis and compliance purposes. The product supports custom plugins to extend detection logic and adapt to unique application landscapes.