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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.

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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.

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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.