Sectors / Energy and utilities
Operational AI risk and sensor-data integrity in safety-critical settings.
When AI informs grid, plant or pipeline decisions, the integrity of sensor data becomes a safety question. aerais governs both sides.
Why it matters
Why governing data and AI together matters in energy and utilities
Energy and utility operators apply AI to forecasting, maintenance, asset health, demand response and field operations. These models run on telemetry from sensors and control systems that were not designed with AI governance in mind.
A drifting sensor, a stale feed or a tampered signal can degrade a model silently. In safety-critical settings, governance must cover data integrity, model change control and the human decision points between them.
Typical questions
Five questions we hear in this sector
- Which operational decisions now depend on AI, and what is the human override path?
- How is sensor and telemetry data checked for drift, gaps and tampering before a model uses it?
- Who approves a model change that touches a safety-related process?
- How do operational technology and IT governance teams share responsibility?
- What is the response plan when an AI-supported decision proves wrong?
Chain of custody
How the chain applies
Data plane controls here are cataloging of telemetry and historian data, quality and drift rules on sensor feeds, lineage from operational systems into models, and access control across operational technology boundaries.
These seam loops tend to matter most:
Quality-gated release
Models for maintenance or dispatch should run only on data that passed integrity checks, with failures visible.
Contaminated-data blast radius
When a sensor fault is found, the blast radius shows which forecasts, alerts and decisions used the affected readings.
Data-to-AI lineage
Post-incident review needs the path from an action back to the readings and model version behind it.
Entry points
Where engagements usually begin
In energy and utilities, a first engagement often centers on data quality and integrity controls on telemetry, followed by change control and oversight for operational AI. Most organizations start from data governance and bring in AI governance as the chain extends. A fixed-scope diagnostic is the usual first step; see engagements for how that is structured.
Regulatory context
Regulatory context, at a high level
Safety and reliability standards shape this sector, for example IEC 62443 for industrial control system security, NERC CIP in North America and NIS2 in the EU. Critical infrastructure regulators set expectations for resilience and incident reporting. The EU AI Act addresses safety components of regulated products and critical infrastructure uses.
This is general context only. Requirements vary by jurisdiction and change over time, so confirm current requirements with counsel and regulators. Alignment with a standard is not certification.
Diagnostic
What a diagnostic would look at here
- Inventory of AI in operational and asset management processes
- Integrity and drift checks on sensor and telemetry feeds
- Change control for models that influence safety-related decisions
- Coordination between operational technology and data governance
- AI incident response and escalation paths
Each item is examined against the chain of custody: whether the control exists, whether it runs inside the systems rather than on paper, and whether it leaves evidence a reviewer can follow. Nothing is assumed from documents alone, and findings are written for executives and boards as well as for practitioners.
The output is a current-state maturity view, risk exposure and a prioritized roadmap. Request a diagnostic to scope it for your organization.
FAQ
Frequently asked questions
Does this replace operational technology security programs?
No. It complements them. aerais focuses on data integrity, model change control and AI oversight, and aligns with your existing OT security and safety frameworks.
What does sensor-data integrity mean here?
Checks that readings are complete, plausible and traceable before a model uses them, with failures visible to the people responsible.
Where would an operator start?
Typically with a diagnostic on the AI uses closest to safety and reliability decisions, and on the telemetry feeds those uses depend on.
Contact
Make your data and AI provable.
Start with a diagnostic: current-state maturity, risk exposure and a prioritized roadmap. We aim to reply within two business days.