InnerActiv is an endpoint-based insider threat detection platform that monitors and controls AI usage and data activities in real-time. The solution focuses on preventing data loss incidents caused by insider threats, including those from disgruntled employees or unintentional user actions. The platform utilizes machine learning, sentiment analysis, and risk analysis to detect potential security events at the endpoint level. It operates with an application-agnostic approach, monitoring data across different applications and storage locations to identify sensitive information and risky behaviors. InnerActiv employs a lightweight endpoint agent that collects and analyzes data from user activities. The system determines data sensitivity by evaluating multiple attributes including application associations, content type, and storage location. Risk sensitivities and alerts can be customized and trained to meet specific organizational needs. The platform includes end-to-end encryption and follows zero-trust architecture principles. It scales to support over 100,000 endpoints and offers customizable configurations for different security requirements. Organizations can configure the system to block or pause risky actions when threats are detected, though this functionality is controlled by administrators. Data privacy features include the ability to redact sensitive information from event results for specific users or workflows. The system collects screen captures and other data only when necessary for security events, with collection parameters controlled by the organization. All retained data is protected with end-to-end encryption throughout its lifecycle.