In Operational Technology (OT) environments, Federated Learning (FL) is increasingly adopted to train Intrusion Detection Systems (IDSs) without sharing raw operational data across facilities. However, model updates may still leak sensitive information about industrial processes, creating privacy risks that remain largely invisible within current OT security architectures. Existing reference models, including the Purdue hierarchy, do not explicitly represent the FL learning lifecycle, making privacy leakage difficult to observe and evaluate. To address this gap, we introduce the ML Plane, a novel architectural abstraction that integrates FL workflows within OT systems, enabling visibility of learning-related information flows that are not explicitly represented in traditional OT architectures. Building on the proposed ML Plane, we outline a privacy auditing framework for FL-enabled OT systems. The framework uses Federated Membership Inference Attacks (FedMIA) as an initial assessment mechanism and is being instantiated within a Smart Grid testbed to support the future evaluation of privacy risks under realistic OT deployment constraints.

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