# Arca Sophia Open-Core: Building Air-Gapped Local AI Inference for Industrial SCADA/PLC Systems > Source: > Published: 2026-08-13 21:38:38+00:00 # 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.