Sternum Threat Detection is an IoT-focused threat detection platform that combines patented embedded integrity verification (EIV™) software with AI-driven monitoring to identify and respond to security threats at the individual device level. The platform collects and triages data from mitigated attacks alongside device-level telemetry, fleet-wide trends, and user activity to provide a panoramic view of security risks across an entire IoT device fleet. Core capabilities are organized into three layers: - Pinpoint Forensics: The EIV™ technology profiles software at runtime, automatically preventing code and memory manipulation. For each blocked attack, it generates 360° forensic detail including attacker IP, targeted asset, and specific line of code involved. - AI-Powered Threat Detection: The AI engine uses multivariate analysis tailored for IoT threats. It auto-trains on connected device behavior to detect anomalies such as suspicious communications, unauthorized access, and brute force attempts, functioning as a continuous monitoring layer. - Custom Security Policies: Users can define security policies using flexible filters including string matches, event counts, time-based thresholds, and whitelists to monitor specific concerns such as failed updates or unauthorized IP connections. The platform can serve as a standalone IoT security management hub or integrate with existing SIEM or SOC platforms via RESTful API. It is positioned as a complement to traditional security stacks, adding XDR-like detection capabilities specifically for IoT environments.