Hypergraph AI is a network detection and response platform that uses a Graph Neural Network (GNN) to model network infrastructure as a topology rather than analyzing sequential log data. It identifies relationships between devices (nodes) and connections (edges) to detect complex attack patterns that signature-based tools cannot identify. Key capabilities: - Graph-based network modeling: Represents the network as a topology to analyze device relationships and detect lateral movement and anomalous behavior. - Agentless deployment: Operates without installing agents on devices, making it suitable for IoT, OT, legacy SCADA systems, and medical equipment such as MRI machines and infusion pumps. - NetFlow/IPFIX processing: Ingests and analyzes network flow data in real-time across hybrid enterprise environments. - Automated L1 analysis: Functions as an autonomous agent that correlates network flows, proposes mitigation actions, and reduces false positives, replacing manual Level 1 SOC analyst tasks. - Pre-trained foundational model: Uses a model pre-trained on large datasets that generalizes to new infrastructure without requiring per-client fine-tuning, targeting deployment in under 30 minutes. - Contextual anomaly detection: Uses semantic relationships between nodes to filter noise and reduce false positives rather than flagging isolated anomalies. Target environments: - Mid and large enterprise IT (hybrid networks) - Medical IoT (clinical devices) - Industrial and legacy OT (SCADA, PLCs) The platform is also positioned for MSSPs and is currently in an early access phase.