Secure the
Backbone of Society
Grid, network, and fleet each generate torrents of operational data, but IT and OT live in separate worlds — and cloud AI is not allowed anywhere near the OT network. That is the problem we solve: one live operational picture of grid, network, and fleet, with real-time anomaly detection and predictive maintenance — frontier sovereign Data & Agentic AI running in air-gapped environments, so you protect energy grids, transport networks, and digital infrastructure without giving up control.
Grid Resilience
Balance loads and detect faults in real-time across distributed energy resources.
OT/IT Convergence
Unified visibility across legacy SCADA systems and modern cloud analytics.
Built for NIS2
Incident detection, audit evidence and risk records that support your NIS2 reporting and risk-management obligations.
Inside Critical Infrastructure
Critical Infrastructure is not one buyer. Each of these pages takes one part of it and describes the problem, what the platform does there and the rules that apply.
Utilities
For: running operations, asset management, security or IT at an energy, water or gas operator, or accountable for its NIS2 obligations.
Energy, water and gas operators: feeder-level forecasting, NIS2 incident reporting and OT data that stays inside the perimeter.
- OT data stays inside
- Built for NIS2
- Agents that ask first
Telecommunications
For: running network operations, customer operations, security or IT at a fixed, mobile or wholesale operator, or accountable for its NIS2 and telecommunications-law obligations.
Fixed, mobile and wholesale operators: alarm correlation, fault prediction, subscriber agents and NIS2 reporting with the data kept inside the operator.
- Network as a graph
- Subscriber data stays home
- NIS2 with evidence
Transport
For: leading operations, asset management, safety, security or IT at a rail infrastructure manager, a port or airport authority, a road operator or a public transport company, or accountable for its NIS2 obligations.
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.
- One model of the network
- Predict before it fails
- Coordinate the incident
The same thirty questions, read with NIS2 and operational technology in mind.
Enterprise AI to production: the checklist
About thirty questions to put to your own team, grouped under grounding, governance, orchestration and sovereignty. Tick what is true for you today and see a score per dimension. One page, yours to print.
Mission-Critical Applications
Ensuring continuity and safety across vital sectors.
Secure Supply Chain Logistics
Logistics
Challenge
Managing sensitive hardware transport requires real-time coordination, but cloud-based logistics platforms expose location data to foreign entities.
Solution
Sovereign agents monitor IoT sensors on edge devices, re-routing shipments based on threat data and weather, communicating only via encrypted channels.
Read more
Resilient automated logistics that functions intermittently offline and leaks no metadata to third-party providers.
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.
Crisis Response Coordination
Emergency Services
Challenge
During natural disasters, fragmented data from various agencies delays critical decision-making and resource allocation.
Solution
A unified command centre powered by AI that aggregates real-time data streams to direct first responders and optimise supply chains.
Read more
Faster response times and more efficient deployment of emergency resources during critical events.
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.
Enterprise AI for critical infrastructure
For a grid operator, a water utility, a transport authority or a telecoms operator, enterprise AI has to cover both halves of the business: the IT side — asset management, work orders, planning, customer operations — and the operational side, where telemetry from the network is the signal that matters. The data is sensor history, alarms, inspection reports and topology, and it describes infrastructure whose failure is a public event rather than an internal one.
It stalls at the boundary. Operational technology is deliberately isolated, and an AI service that needs outbound connectivity cannot cross that line. NIS2 makes the operator answerable for supply-chain risk and incident reporting, so adding an outside dependency to the control estate is a regulatory decision rather than an IT one. And the historian's tags are rarely described well enough for a model to learn anything dependable from them.
Three things have to hold before it reaches production. The platform runs as a sovereign cluster inside the operator's own perimeter, air-gapped where the control network requires it. Assets, sensors and services are described once, so forecasts and agents reason over the same model. And the control room keeps the decision, with every proposal and approval logged. That is enterprise AI for critical infrastructure.
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.
Your Data, Your AI, Your Control
Deploy the Scrydon platform where it makes sense for you — from air-gapped environments to public cloud — with sovereignty, compliance, and auditability built in.
Deployed on-premises, air-gapped or in a sovereign cloud, no data leaves your jurisdiction. No black-box AI. No compromises on control.
This is sovereignty by design.
What these outcomes actually run on
Everything above is delivered by one platform, designed to keep running when the network does not. For the architects and engineers evaluating it, these are the pieces that matter.
Sovereign Foundations
The zero-trust foundation the whole platform sits on — the same stack from air-gapped to cloud.
On-Premises
The platform in your own datacentre, on your own hardware.
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.
Laws and standards that matter for Critical Infrastructure
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: Applies to dual-use and civilian administrative AI. AI used exclusively for military, defence or national-security purposes is outside its scope (Art. 2(3)).
How the platform supports itISO 27001 · ISO/IEC 27001
Applies to: Any organisation that operates an information security management system — routinely required of software vendors, service providers and regulated enterprises by customers, regulators and procurement.
How the platform supports it
Read for NIS2: what still runs with the cable unplugged, and who holds root when it does.
“Sovereign” is not a label on a datasheet
It is five tests you can run against a live system. Score any vendor on jurisdiction, keys, operating staff, disconnected mode and exit — plus whether the offer covers all four layers, and whether each answer was demonstrated or asserted. Twelve questions, nothing leaves your browser.
Score a vendor- Jurisdiction
- Keys
- Operating staff
- Disconnected mode
- Exit
- Four layers
Run it once per name on the shortlist — including the incumbent, and including us.
Frequently asked questions
What does sovereign AI for critical infrastructure mean, and why does air-gapped or OT isolation matter?+
How does the platform support NIS2 and cyber resilience, and is it auditable?+
Can we run AI in air-gapped or OT environments alongside our operational systems?+
How can agentic AI improve grid resilience, renewables integration and resilient supply chains?+
We ran an AI pilot — how do we move to organisation-wide, governed AI across our operations?+
How are identity, attribution and audit handled for AI agents acting on critical systems?+
How do we keep operational data protected from the cloud operator?+
Is the platform model-agnostic and able to run open-weight models on-premises to avoid lock-in?+
Is our historical SCADA and sensor data actually usable for AI, or does it need cleaning up first?+
Can we give an AI agent narrow, auditable access to OT systems without a shared service account?+
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.
Scale – From personal AI to organisational AI: why copilots plateau, and what an AI operating system changes
13 Oct 2026, 09:00
Part 4 of the Sovereign AI series, for CIOs, COOs and the people who own processes and AI Centres of Excellence — in any sector. Personal AI raises the productivity of a person; organisational AI changes the outcome of a process. The gap is not a better model but four missing things: shared context (an ontology, not each person's chat history), governed action (agents that act on systems under policy), identity and permissions that follow the work across teams, and evidence a board or regulator will accept. A live contrast between a personal assistant and the AI OS on the same question, then one end-to-end process run by agents with people in the loop.
Partners
