A GPU-native SIEM that detects threats while the data is still moving. Instead of collecting logs and correlating them later, it tokenizes, classifies and enriches every event in flight on the GPU — semantic AI detection, not regex chains — then indexes to a sharded, authenticated store. Tens of thousands of events per second on a single edge node, on-prem.
Events are tokenized, classified and scored on the GPU as they stream in — alerts fire near ingestion, not after a delayed search job.
A BERT classifier reads intent and meaning in raw log text, catching threats that static rules and regex miss.
A dead-letter queue protects failed batches, retention keeps storage bounded, and authenticated, sharded indexing keeps search fast.
Logs land in a Kafka stream.
Workers tokenize on the GPU.
BERT inference scores intent.
Enriched incidents to search.
DDoS floods and intrusion attempts are detected and deflected at a shielded perimeter — before they reach what matters.
High-throughput Kafka in KRaft mode (no ZooKeeper) feeds parallel consumers — backpressure-safe at tens of thousands of events per second.
An inference server runs the detection model on the GPU in batches, so classification scales with parallelism instead of CPU cores.
Scores and metadata are attached, then incidents are written to an authenticated, multi-shard search index for fast investigation.
Dead-letter queue, health checks, retention enforcement and authentication keep the pipeline resilient and storage bounded.
Logs, endpoints, identity and network events are classified for intent — credential abuse, lateral movement and exfiltration patterns surfaced in flight.
Operational-technology and device telemetry are watched on the same pipeline, so anomalies on the plant floor and at the edge are caught beside IT threats.
IT and OT detections land in one store with shared scoring and timelines — correlation across both estates, not two disconnected tools.
Turnkey Edge-AI — fixed time, fixed cost, full responsibility.
