Sovereign AI for Utilities
Grid, water and gas operators run the systems everything else depends on, under NIS2 and a regulator who will ask for the record. AI on that data has to stay inside the perimeter and explain itself. That is what the platform is built to do.
OT data stays inside
Runs on the operator's own infrastructure. Grid topology, telemetry and metering never leave the perimeter, connected or not.
Built for NIS2
Incidents correlated against your own services and assets, early warnings drafted with evidence attached, every review logged.
Agents that ask first
Every agent acts under a scoped identity and a policy. Anything that touches a switch, a valve or a contract goes through your approval.
A distribution operator's day is now decided feeder by feeder: rooftop solar, heat pumps and charging points have made the network two-way, and NIS2 has made every outage a reportable event. The data to run it well already exists in SCADA, metering, GIS and asset systems. What is missing is one place where it comes together, where AI can act on it, and where none of it leaves the operator's control.
Read this if you're running operations, asset management, security or IT at an energy, water or gas operator, or accountable for its NIS2 obligations.
For utilities, Scrydon is the sovereign AI and data platform that brings SCADA, metering, GIS and asset data into one governed model, runs forecasting and agents on it inside the operator's own infrastructure, and produces the incident, activation and audit records NIS2 and the regulator expect.
It runs identically on-premises, in a national sovereign cloud or fully disconnected, so the same platform serves the control room, the asset planners and the security team, and the operational-technology boundary stays where the operator drew it.
The grid became two-way. The tooling did not.
Ten years ago a distribution operator planned capacity by region and by year. Today the binding constraint is a single feeder on a single afternoon, when rooftop solar peaks, heat pumps idle and a street of charging points comes online together. The answer to whether that feeder holds lives in metering, weather, switch state and the flexibility contracts available in that area, and it has to be recomputed continuously.
At the same time the regulatory clock changed. Under NIS2 an outage or a cyber event is a reportable incident with a 24-hour early warning and a 72-hour notification, and both start from the moment the operator knows. Knowing depends on correlating what SCADA, the SIEM, the ticketing system and the change calendar already log, and doing that during the incident rather than after it. None of this data can be sent to a foreign AI service: metering data is personal data and topology is national-security data.
Feeder-level decisions — Congestion is now local and hourly. The forecast has to be at the resolution of the asset, not the region.
Data in silos that must not merge naïvely — SCADA, historian, GIS, EAM and metering each have a good reason to be separate. They still need one model.
A reportable clock — NIS2's 24- and 72-hour deadlines start when you know, and knowing depends on correlating what you already log.
Data that cannot leave — Smart-meter data is personal data; topology is national-security data. Neither can be sent to a foreign AI service.
One model of the network, and agents that work on it
The platform starts from a model of the network in the operator's own terms: substations, feeders, transformers, connections, meters, contracts and the services they carry, mapped from GIS, the asset system, the historian and the metering platform without migrating any of them. Telemetry, alarms, weather, work orders and market signals land on that model as they arrive, so a question about a feeder has one answer whichever system the detail came from.
On top of that model, decision intelligence turns a predicted overload or a degrading asset into a recommended, costed action, and agents draft what follows: the flexibility activation, the work order, the NIS2 early warning. Each of those goes through the operator's approval workflow. A named person approves, and the draft, the approval and the reasoning are logged together. The control room keeps its authority; it gains the preparation.
- 1
Model
An ontology of substations, feeders, assets, connections, contracts and services, mapped from the systems you already run.
- 2
Fuse
Telemetry, alarms, metering, weather and work orders land on that model continuously, so a question about a feeder has one answer.
- 3
Decide
Forecasting and decision intelligence turn a predicted overload or a degrading asset into a recommended, costed action.
- 4
Act, with approval
Agents draft the activation, the work order or the NIS2 notification. A named person approves. Everything is logged.
The OT boundary is a design constraint, not a deployment option
A utility's operational-technology boundary is not a preference; it is the line the security authority audits. The platform is built to sit on the right side of it. It runs on the operator's own hardware, in a national sovereign cloud or fully air-gapped for the control network, with open-weight models served locally so no inference call, licence check or update path leaves the perimeter.
Agents are treated as identities, not scripts. Each one authenticates, carries scoped permissions per system and per action, and is observed like any other actor. An agent that reads telemetry cannot write to a controller unless a policy says so and a person approves. The audit log, the policy decisions and the approvals that result are the evidence NIS2 and ISO 27001 supervision asks for, produced by running the platform rather than assembled for the audit.
On-premises or disconnected — The full platform runs on your hardware, in a national sovereign cloud, or air-gapped for the control network.
Open-weight models, served locally — No inference call leaves the perimeter. Model choice stays yours, and switching does not mean re-architecting.
Scoped agent identity — Agents authenticate like operators, with permissions per system and per action, so an agent that reads telemetry cannot write to a controller.
Evidence by construction — The audit log, the policy decisions and the approvals are the NIS2 and ISO 27001 evidence, not a document written afterwards.
Keep an eye on this space
What changed for your sector in European AI sovereignty, and what we learned in the field. A few times a year, no drip campaign.
Use cases for utilities
Grid, water and gas operations on one sovereign platform.
Predictive Infrastructure Maintenance
Public Works
Challenge
Reactive maintenance of roads, bridges, and utilities leads to costly emergency repairs and service disruptions.
Solution
Analytics models process data from IoT sensors and historical reports to predict failures before they happen, scheduling preventative maintenance.
Read more
40% reduction in emergency repair costs and extended lifespan of critical public infrastructure.
Predictive Grid Maintenance
Energy & Utilities
Challenge
Equipment failures in remote substations cause cascading blackouts and costly emergency repairs.
Solution
Edge AI agents analyse vibration and thermal sensor data locally, predicting component failure weeks in advance without uploading terabytes of raw data.
Read more
40% reduction in unplanned downtime and optimised maintenance schedules.
Water Quality Monitoring
Public Utilities
Challenge
Contamination events in municipal water supplies are often detected too late, risking public health.
Solution
Distributed sensor agents monitor chemical composition in real-time, automatically isolating affected pipe sections and alerting authorities instantly.
Read more
Immediate containment of contamination events and guaranteed water safety.
AI-Ready Sensor & OT Data Foundation
Data Engineering
Challenge
Grid, water and transport operators sit on years of SCADA and sensor history, but it is too fragmented and poorly labelled for AI models to use reliably.
Solution
An AI-ready data pipeline cleans, contextualises and grounds OT and sensor data in the ontology before it reaches any model, so agents reason over trustworthy, well-described data rather than raw tag soup.
Read more
Predictive models and agents reach production readiness faster, built on a data foundation operators can actually trust.
Scoped Agent Identity for OT Systems
Security & Access Control
Challenge
Giving an AI agent access to SCADA or historian systems is high-risk when every agent shares one broad service account instead of its own scoped identity.
Solution
Each agent authenticates under its own federated identity with least-privilege, time-boxed permissions, so an agent that reads sensor telemetry can never also write control setpoints unless explicitly authorised.
Read more
Agents operate safely alongside OT systems with a fully attributable permission model, closing off a major class of agentic-AI risk in operational environments.
Human-Agent Outage Response Coordination
Incident Response
Challenge
An outage response spans multiple teams and shift changes, and each handoff between an agent's triage and an operator's decision restarts the coordination from scratch, costing minutes that matter during an active incident.
Solution
The AI OS runs outage response as one coordinated process: agents triage telemetry and stage response actions, operators approve or override at defined checkpoints, and the full incident state carries across shift changes automatically.
Read more
Faster, better-coordinated outage response with a single continuous incident record instead of fragmented handoffs between teams and shifts.
What Utilities has to comply with
The regulations that decide whether AI can run on this data at all. Each page lists what the framework asks and which platform controls answer it — properties and refusals, not a checklist.
NIS2 · NIS2 Directive
Applies to: Essential and important entities across critical sectors — energy, transport, water, health, digital infrastructure, public administration, manufacturing and more — and their supply chains.
How the platform supports itCRA · Cyber Resilience Act
Applies to: Manufacturers, importers and distributors of products with digital elements — hardware and software — placed on the EU market, including software vendors and the organisations that build connected products on top of them.
How the platform supports itAI Act · EU AI Act
Applies to: Providers, deployers, importers and distributors placing AI systems on the EU market or whose AI output is used in the EU.
How the platform supports itGDPR · General Data Protection Regulation
Applies to: Any organisation that processes the personal data of people in the EU/EEA, whether established in the Union or offering goods, services or monitoring from outside it.
How the platform supports it
What these outcomes actually run on
A utility architect's first questions are whether it runs on the OT network, how agents are kept away from controllers, and how the network is modelled. These are the pages that answer them.
Sovereign Foundations
The zero-trust foundation the whole platform sits on — the same stack from air-gapped to cloud.
Air-Gapped
The full platform running offline, on networks with no route to the internet.
AI OS
The runtime that maps a process to steps, routes each step to a system, an agent or a person, and gives it the context to act.
Digital Twin
A live model of a physical system you can query, simulate against and plan with.
AI Observability
Monitoring, tracing and evaluation for agents and AI workflows, so failures are diagnosable.
Identity
Federated identity and zero-trust access — for people and for agents, under the same policy.
Frequently asked questions
How does the platform help a utility meet NIS2 incident-reporting deadlines?+
Can it run on the operational-technology network, disconnected from the internet?+
Does this replace our SCADA, historian or asset-management system?+
How do you keep an AI agent from acting on the grid without a human?+
Is smart-meter data safe to use for forecasting?+
We have run AI pilots on outage prediction. What is different here?+
Do you partner on tenders and framework contracts?+
Prefer to write? Email hello [at] scrydon.com and we will get back to you.
Keep going
Three routes from here: the rules you will be measured against, the platform these outcomes run on, and the sessions where we walk through them.
The rules that apply
Each regulation, what it asks, and the controls the platform provides for it.
The platform behind it
The AI Operating System, Analytics and Sovereign Foundations, page by page.
Every use case
All of them in one place, grouped by the sector that knows them best.
How – Inside the AI OS: running governed agents on your own cluster, live
17 Sept 2026, 09:00
Part 2 of the Sovereign AI series, for engineers and architects. No slides after minute five: an agent gets an identity and scoped permissions, calls tools over governed MCP, retrieves from the ontology rather than raw tables, runs inside the sandbox and is stopped when it steps outside policy, and everything lands in the audit trail — then the same stack brought up on a disconnected network. Properties and refusals, shown rather than claimed.
Other parts of Critical Infrastructure
The other dedicated pages under Critical Infrastructure, and the sector overview they hang off.
Telecommunications
Fixed, mobile and wholesale operators: alarm correlation, fault prediction, subscriber agents and NIS2 reporting with the data kept inside the operator.
Read the pageTransport
Rail, ports, airports, roads and public transport: one model of the network, predictive maintenance, flow optimisation and incident response on OT data that stays inside the operator.
Read the page
Partners
