How Cloud Connected AI Products Run On-Prem

The Purdue Model remains a widely used framework for separating industrial operational technology from enterprise IT through layered network architecture, with the critical IT/OT boundary typically protected by a demilitarized zone to limit the effects of cyber incidents on physical processes. Although its original air-gap assumptions have been challenged by cloud connectivity, IoT, and AI adoption, the model continues to inform standards such as ISA-95 and IEC 62443, increasingly supplemented by zero-trust controls, device visibility, and brokered cross-layer communications. AI deployments create particular challenges because edge systems need to process data close to machinery while models, monitoring, and selected results may need to move across network zones. Roboflow describes several deployment options intended to accommodate these requirements, including cloud batch processing, on-premises inference connected through APIs, fully air-gapped deployments using manually transferred model artifacts, and DMZ-based gateways that centralize and log communications between edge devices and external services.

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