Government & Research Data Spaces — Sovereign AI at Scale

Cross-agency collaboration with ephemeral data enclaves and policy‑as‑code. National compliance logs, public‑audit ready.

Why Public-Sector Data Spaces Need Sovereign AI

Governments and publicly funded research consortia are under pressure to share data across borders and sectors (health, environment, education, mobility, procurement) while respecting strict sovereignty and privacy rules. The EU’s data‑strategy initiatives—Gaia‑X and the European Health Data Space (EHDS)—aim to create federated infrastructures where participants retain control over their data. EHDS, for example, sets out a common framework for the use and exchange of electronic health data across the EU, enabling citizens to access and share health data across borders and allowing secure secondary reuse for research and policy. Complementary projects like FAIR Data Spaces show how Gaia‑X can be combined with national research infrastructures (NFDI) to build cloud‑based data spaces for science and industry.

At the same time, cross‑sector initiatives such as the Green Deal Data Space propose open ecosystems for resilience and sustainability, with modules for secure data sharing rooms and privacy‑preserving data exchanges in supply‑chains, energy, and crisis‑management. The Prometheus‑X project demonstrates how trustworthy AI assessment platforms can audit algorithms on educational data while ensuring the data never leaves the provider’s control and that access rights are enforced. In Africa, pan‑continental AI strategies emphasise data sovereignty—who owns, controls and manages data—and the need to build citizen trust and local innovation capacity. These initiatives illustrate the political imperative: public‑sector AI must be trusted, federated, and sovereign by design.

Challenges for Public Agencies and Research Consortia

  1. Data sovereignty & localisation: Governments cannot simply centralise sensitive data (e.g., healthcare records, environment measurements, education outcomes) in a single cloud. The Gaia‑X value proposition highlights that organisations must retain control over their data while benefiting from collaboration. African strategies further stress the need to keep data within national borders and maintain local control.
  2. Cross-border collaboration: EU regulations like EHDS call for cross‑border infrastructures enabling primary and secondary uses of data and Council decisions emphasise improving cross‑border access to digital health services and products. Similar ambitions exist for environment, mobility and education domains.
  3. Heterogeneous regulations: Public projects must simultaneously comply with GDPR, the AI Act, sectoral regulations (e.g., EHDS, Data Governance Act, Green Deal), and, increasingly, national AI strategies (e.g., AU Agenda 2063). Ensuring that every analytical task respects these rules is complex.
  4. Trust & auditability: Civil servants and researchers need to justify algorithmic decisions to citizens, auditors and legislators. Tools like the Prometheus‑X audit platform show the need for transparent AI assessments where profiling algorithms can be easily audited and compared, generating trust and ethical notation.
  5. Multi-domain participation: Projects like FAIR Data Spaces combine industry and academia. The Green Deal Data Space includes supply chains, hospitals and energy providers in a single environment. Aligning data models, semantics and governance across domains is a major technical challenge.

AffectLog’s Sovereign AI Platform for Government & Research Data Spaces

To address these challenges, AffectLog has deployed its Sovereign AI platform as the backbone of a multi‑country pilot spanning EU and African partners. The pilot federates datasets from national health ministries, environment agencies, education departments, transport authorities and research consortia. Participating nodes include:

  • EU ministries contributing to the European Health Data Space and Green Deal Data Space projects;
  • Research institutions participating in the FAIR Data Spaces demonstrator (combining Gaia‑X and NFDI);
  • African agencies aligned with Agenda 2063’s data sovereignty goals;
  • The Prometheus‑X Trustworthy AI initiative evaluating educational and skills‑matching algorithms.

The platform integrates three technical pillars, each benchmarked and validated by domain experts:

1. Ephemeral Sandbox Orchestration Protocol

For every analysis or model training, the platform spins up ephemeral enclaves inside each data owner’s infrastructure (government cloud, national supercomputer, or on‑premise data centre). These enclaves load local datasets—e.g., health records, environmental sensor streams, education registries—and execute the AI task. Upon completion, they tear down automatically, leaving zero data residue in line with Gaia‑X’s emphasis on sovereignty. This architecture mirrors the secure data‑sharing rooms and privacy‑preserving data hubs envisaged by the Green Deal Data Space and ensures that sensitive data never leaves the jurisdiction.

During training, only encrypted model updates or aggregated statistics are exchanged. For example, when building a cross‑border pandemic surveillance model, each ministry computes infection‑prediction updates locally and shares only the gradients. Similarly, environmental agencies jointly train extreme‑weather models, combining local climate sensors without centralising raw data. In education, the Prometheus‑X use case runs educational analytics inside the LOLA platform, where algorithm evaluations occur on local data and produce audit reports.

2. RegLogic Compliance DSL

AffectLog’s RegLogic engine codifies ≈400 regulatory clauses from the EU AI Act, GDPR, ISO/IEC 42001, OWASP AI guidelines and sector-specific laws (e.g., EHDS, Data Governance Act). Each AI job is automatically mapped to relevant obligations:

  • For health data, the engine checks EHDS compliance—ensuring primary use (clinical care) and secondary use (research) remain separated and that cross‑border rules are respected.
  • For environmental projects, it enforces the Green Deal requirements for transparency and public-good data use.
  • For education analytics, it integrates the Prometheus‑X trust metrics and ensures AI systems align with EU AI Act risk classifications.
  • For African partners, RegLogic includes national data protection laws and reinforces Agenda 2063’s data sovereignty principles.

All policy checks and decisions are logged to an immutable audit ledger, producing national compliance logs that are public-audit ready. Regulators can review every training round, consent decision and model inference, satisfying transparency mandates and enabling FOIA-style oversight. When new regulations emerge (e.g., Data Act, African AI frameworks), the engine updates its clause library, ensuring future-proof compliance.

3. Bias-Aware XAI Pipeline

Public-sector AI must not only be legal but also fair and explainable. AffectLog’s Bias‑Aware XAI pipeline runs across the federated network:

  • Each node computes SHAP feature attributions on its local data, revealing which variables (e.g., age, region, pollutant levels) most influence predictions.
  • Counterfactual analysis tests whether altering sensitive attributes changes outcomes (e.g., if a climate impact forecast differs solely because of a country’s GDP).
  • Causal graphs separate correlation from causation, ensuring models are not making policy decisions based on proxies for protected traits.

These insights are aggregated and presented in dashboards accessible to agency data stewards and oversight bodies. The Prometheus‑X trust toolbox already uses similar techniques to produce audit reports and ethical notations for educational algorithms. By integrating this pipeline, AffectLog ensures that cross‑agency models meet fairness requirements and align with public‑sector ethics frameworks.

Real-World Examples from the Pilot

  1. Cross‑border pandemic alerting: Health ministries from France, Spain and Senegal train a federated early‑warning model on local hospital visits, lab results and environmental factors. EHDS rules separate primary (care) and secondary (research) use while allowing cross‑border analytics. RegLogic ensures each use case complies with both EU and Senegalese data laws. The Bias‑Aware pipeline highlights that socio‑economic indices do not unfairly skew predictions.
  2. Environmental resilience: Agencies participating in the Green Deal Data Space create a joint model to forecast flood risks. Each agency uses its own hydrology sensors and land data; the federated model is trained across nodes via the sandbox protocol. The resulting forecasts feed into cross‑agency emergency planning and are published on an open portal. The platform’s secure data sharing mirrors the Green Deal modules (secure sharing rooms, privacy‑preserving data exchange).
  3. Skills and education analytics: Prometheus‑X and national education ministries run algorithm audits on student‑skills matching platforms. The LOLA platform provides a secure environment where data never leaves the provider’s control and where algorithm contributors receive transparent audit reports. AffectLog’s integration allows these audit results to be logged alongside regulatory compliance (AI Act, GDPR) and cross‑border educational data-sharing pilots.
  4. Research data space for biodiversity: Universities participating in the FAIR Data Spaces project combine ecological datasets across countries. AffectLog orchestrates federated analytics on species distribution models, combining Gaia‑X’s federated infrastructure with NFDI research data governance. RegLogic ensures that data licensing and ethical use (e.g., Indigenous data rights) are enforced. The audit ledger records all queries and models, enabling reproducible science and public accountability.

Outcomes and Adaptability

The ongoing pilot has shown that public agencies can collaborate across borders and sectors without compromising sovereignty. By leveraging ephemeral enclaves and policy‑as‑code, participants share insights, not raw data; cross‑border analytics become routine instead of exceptional. Compliance audits that once took months are now automatic and continuous. Regulators can inspect logs and dashboards with confidence, while citizens gain assurance that their data rights and privacy are respected.

The architecture is fully extensible. New sectors (transport, justice, taxation) can be added by defining additional schemas and policies. Additional countries can join by deploying local nodes and adopting the RegLogic clause library; African partners, for instance, can integrate their own data protection and sovereignty clauses. The system also interfaces with broader initiatives—Gaia‑X’s cross‑border collaboration frameworks, EHDS’s cross‑border health infrastructure and FAIR Data Spaces’s science‑industry linkage—making it a blueprint for future sovereign AI ecosystems.

Conclusion

This case study demonstrates how AffectLog enables government and research bodies to build sovereign, compliant, and transparent AI ecosystems that support national and international data spaces. By combining ephemeral sandbox orchestration, RegLogic policy‑as‑code, and a Bias‑Aware XAI pipeline, the platform empowers public agencies to collaborate across borders and sectors while preserving data sovereignty, meeting complex regulatory requirements, and maintaining public trust. The pilot’s success across health, environment, education and research domains shows that sovereign AI is not theoretical—it’s operational and ready for scale.