Arca Sophia Open-Core: Building Air-Gapped Local AI Inference for Industrial SCADA/PLC Systems

Arca Sophia Open-Core is an open-source reference architecture for running quantized LLMs locally on air-gapped industrial SCADA/PLC systems. The project addresses the trade-off between cloud latency and plant security by containerizing inference at the edge and adding a deterministic safety layer to prevent raw model output from directly controlling actuators.

Arca Sophia Open-Core: Building Air-Gapped Local AI Inference for Industrial SCADA/PLC Systems Integrating Artificial Intelligence into Operational Technology OT and Industrial Control Systems ICS presents a fundamental engineering trade-off: Cloud latency vs. Plant Security. In continuous industrial processing, sending telemetry to public cloud LLMs breaks air-gapped network designs and introduces non-deterministic network delays. To solve this, we designed Arca Sophia Open-Core —an open-source AGPLv3 reference architecture for executing quantized LLMs directly at the edge, fully containerized and bound by physical safety rules. Key Architectural Challenges in Industrial Edge AI - Strict Air-Gap Requirements : OT networks cannot expose open WAN ports or send raw telemetry to third-party endpoints. - Resource Constraints : Edge nodes in industrial cabinets operate with capped hardware specifications e.g., 4 vCPUs, 8GB RAM . - Non-Deterministic Risk : Raw LLM output cannot directly trigger PLC actuators without a deterministic safety layer. The Stack & System Architecture Arca Sophia Open-Core resolves these constraints through a modular Docker setup: - Inference Engine : Runs 4-bit quantized GGUF models e.g., Qwen3-8B-Instruct via local backends without internet access. - PLC Simulation Layer : Isolated container simulating real-time SCADA telemetry and register reads. - Deterministic Shield SILIC-ETHIC : A rules-based containment layer that sanitizes model inferences against physical operational thresholds before any output is passed down the pipeline.