Securing AI on the Edge of an Energy Provider's OT Network

An Australian electricity distributor introducing machine-learning analytics for demand forecasting and predictive maintenance across its corporate IT and operational technology (OT) environments.

As a regulated critical-infrastructure operator under the Security of Critical Infrastructure (SOCI) Act, the provider needed to adopt AI analytics without creating new paths between corporate IT and the OT network that keeps the lights on. Leadership needed assurance the models and their data flows couldn't become a bridge for an attacker.

• Mapped every data flow between OT, corporate IT, and the new ML pipelines.
• Tested the analytics platform and its model interfaces for evasion and data poisoning.
• Assessed the IT/OT boundary against the provider's SOCI risk-management obligations.
• Built a prioritised AI adoption and security uplift roadmap for the board.

• A model-retraining pipeline could pull data across the IT/OT boundary without segmentation.
• Analytics service accounts held broader OT read access than the use case required.
• Poisoned input data could degrade predictive-maintenance outputs undetected.

The provider adopted its AI analytics with segmented data flows, least-privilege service accounts, and input-integrity monitoring, demonstrably reducing risk to the OT environment and producing evidence aligned to its SOCI obligations.