Sovereign AI for Public Health
Population data is the most sensitive data a state holds and the most useful in a crisis. The platform lets health authorities, agencies and insurers analyse it, share it under a legal basis, and act on it, without pooling it or sending it anywhere.
Legal basis in the query
Every cross-controller analysis carries the legal basis, the purpose and the retention period, and the data product enforces aggregation or pseudonymisation where the law requires.
Federate, do not pool
Registries, claims and surveillance data stay with their controllers and are published as governed products; the analysis runs across them.
From signal to action
An outbreak signal or a shortage forecast becomes a recommended allocation or campaign, routed to the person who approves it.
A health authority sees the population through registries, claims, surveillance feeds, lab reports and the hospitals it oversees, each with its own controller and its own legal basis. Answering a question about an outbreak, a shortage or a screening programme means bringing those together, fast, without breaking the law that keeps them apart. The platform does that through governed data products and federated analysis, and turns the answer into an action a person approves.
Read this if you're leading data, digital, surveillance, crisis preparedness or IT at a national or regional health authority, a public-health agency, a health insurer or a health data access body.
For public health, Scrydon is the sovereign AI and data platform that lets authorities, agencies and insurers analyse population, claims and surveillance data across controllers through governed data spaces and federated analysis, run crisis and screening logistics on it, and report to regulators, all inside state-controlled infrastructure with a named legal basis on every query.
It runs in a national sovereign cloud or on the authority's own infrastructure, and the same data-space model serves the hospitals, insurers and research bodies the authority works with, each keeping its own data.
Many controllers, one population, and a clock in a crisis
A health authority sees the population through many windows: disease registries, claims data at the insurers, laboratory reports, syndromic and wastewater surveillance, the hospitals it oversees and the screening programmes it runs. Each window has its own controller and its own legal basis, which is the law working as intended, and it is also why the traditional answer, a central health data warehouse, is either unlawful or empty of the data that matters.
The cost shows in a crisis. Outbreak signals are correlated by epidemiologists after the fact; vaccines, protective equipment and screening capacity are allocated on stale data in the weeks that decide outcomes; and European, national and regional regulators each want the same population described their own way, on their own schedule. Any AI that helps has to bring the windows together fast, without breaking the law that keeps them apart, and has to leave the allocation decision with an accountable official.
Data split by law — Registries, claims, surveillance and hospital data each have a controller and a legal basis. A central warehouse is either unlawful or empty.
Surveillance by hand — Lab reports, syndromic feeds and wastewater signals are correlated by epidemiologists after the fact.
Logistics under pressure — Vaccines, protective equipment and screening capacity are allocated on stale data in the weeks that matter most.
Reporting to everyone — European, national and regional regulators each want the same population described their own way, on their own schedule.
Governed products, federated analysis, approved actions
The platform reverses the direction of the warehouse. Each controller keeps its data where it is and publishes governed data products into a data space, with schema, quality and access policy described in a shared health ontology. A public-health question is a query across those products, executed under an authorisation that names the legal basis, the purpose and the retention period, and logged. Where the law allows only aggregate or pseudonymised results, the product enforces it, not a guideline.
On top of that, surveillance feeds are fused with data fusion and modelled continuously, so an outbreak signal or a shortage forecast surfaces before the weekly report and is traceable to its sources. Decision intelligence turns the signal into a recommended allocation or campaign, routed to the official who approves it, and the reasoning is recorded. Regulatory reports to European, national and regional bodies are drafted from the same model and checked by a person.
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Publish
Each controller publishes governed data products from its own systems, with schema, quality and access policy described in a shared health ontology.
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Federate
A public-health question is a query over those products, executed under a named legal basis and logged, with aggregation or pseudonymisation enforced in the product.
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Detect and forecast
Surveillance feeds are fused and modelled continuously, so outbreak signals and shortage forecasts surface before the weekly report.
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Act with approval
Decision intelligence turns a signal into a recommended allocation or campaign; an accountable official approves, and the reasoning is recorded.
Population data stays with the state, and every use is accountable
Population health data stays with the state. The platform runs in a national sovereign cloud or on the authority's own infrastructure, with open-weight models served locally, and on shared infrastructure confidential computing keeps health data encrypted while it is processed. There is no central copy: the analysis travels to the data, not the other way round.
Every use is accountable. A query carries its legal basis and lands in an immutable audit log; agents act under scoped identities and every action is attributed; allocation and campaign decisions are taken by a named official whose approval is recorded with the reasoning. That record is what the data protection authority, the court of audit and the EU AI Act ask for, and it is produced by running the platform under GDPR and NIS2 rather than assembled afterwards.
State-controlled infrastructure — Runs in a national sovereign cloud or on the authority's own infrastructure, with open-weight models served locally.
No central copy — Data spaces replace the warehouse: each controller keeps its data, and the analysis travels to it.
Encrypted in use — Confidential computing keeps health data encrypted while processed on shared infrastructure.
Evidence for the auditor — Every query carries its legal basis and lands in an immutable log, which is what the data protection authority and the court of audit ask for.
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 public health
Surveillance, crisis logistics and regulatory reporting on one sovereign platform.
Healthcare Regulatory Reporting
Compliance
Challenge
Healthcare providers struggle with complex, ever-changing regulatory requirements and manual reporting processes.
Solution
Automated agents compile and submit compliance reports by aggregating data from disparate clinical systems with full audit trails.
Read more
100% on-time regulatory submissions with significantly reduced administrative burden.
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.
Ontology-Based Patient & Provider Master Data
Master Data
Challenge
The same patient can appear as different records across the EHR, billing and scheduling systems, and providers are identified inconsistently across departments, so linking a patient's full history means manual matching that risks missing or merging the wrong records.
Solution
An ontology resolves patient and provider identity once across every clinical and operational system, so every record referring to a person actually points to the same entity.
Read more
Clinicians and agents work from one reliable patient record instead of a manually reconciled patchwork across systems.
Crisis Supply Allocation & Distribution
Emergency Logistics
Challenge
During a health or civil emergency, allocating scarce supplies — vaccines, PPE, generators — across regions is decided from stale spreadsheets each region reports differently, so allocation lags days behind actual need.
Solution
Decision intelligence connects live stock levels, demand forecasts and distribution capacity in one ontology-grounded picture, recommending allocations and routing each through the appropriate approval before dispatch.
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Allocation decisions made in hours against data every region trusts, with a complete audit trail of who approved what and why.
What Public Health 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.
GDPR · 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 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 itNIS2 · 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 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
What these outcomes actually run on
A health authority's architect asks how controllers federate, where the data is encrypted and how a signal becomes an approved action. These are the pages that answer those questions.
Data Spaces
Data shared across organisational boundaries without handing over ownership of it.
Sovereign Foundations
The zero-trust foundation the whole platform sits on — the same stack from air-gapped to cloud.
Analytics
Self-service analytics over a governed semantic model, so numbers mean the same thing in every report.
Cognitive Enterprise
The ontology that links your systems, data and processes into one model of how the organisation actually works.
Confidential Compute
Workloads that stay encrypted while they run, inside hardware-attested enclaves.
AI Governance
Policy, data-loss prevention and audit applied to every AI action, with the evidence an assessor can read.
Frequently asked questions
How can we analyse across registries, claims and hospitals without a central health data warehouse?+
Can the platform support outbreak surveillance?+
How does it help allocate vaccines, equipment or screening capacity in a crisis?+
How is GDPR respected when several controllers are involved?+
Can insurers and research bodies take part on the same terms?+
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 Healthcare
The other dedicated pages under Healthcare, and the sector overview they hang off.
Hospitals & Care Providers
Hospitals, care groups and clinics: clinical decision support, patient flow and care coordination on one clinical ontology, inside the institution, with the clinician deciding.
Read the pageLife Sciences
Pharma, biotech, medtech and research: AI on trial, lab and manufacturing data inside your perimeter, with data-loss prevention and egress controls deciding what may ever leave.
Read the page
Partners
