Research Hub — Healthcare Compliance

Healthcare AI sits at the nexus of privacy, safety and patient rights. In the EU, the Artificial Intelligence Act classifies AI systems used as safety components of medical devices as high‑risk. Article 6(1)(b) specifies that an AI system is high‑risk if it serves as a safety component of a medical device or in‑vitro diagnostic (IVD); most AI‑enabled medical devices therefore fall into the high‑risk category[1]. High‑risk classification triggers stringent obligations: providers must implement risk‑management systems, ensure data governance and technical documentation, perform human oversight, maintain transparency, and continuously monitor post‑market performance[2]. These requirements come on top of existing Medical Device Regulation (MDR) and IVDR quality systems[3]. Compliance also intersects with GDPR—patient data is sensitive personal data, and controllers must ensure lawful processing, purpose limitation and data‑minimisation.

Compliance Context

·   Risk classification & MDR/IVDR interplay: AI components used in or as medical devices that require Notified Body review under MDR/IVDR are deemed high‑risk[1]. Self‑certified Class I devices and non‑sterile Class A IVDs are not high‑risk[4]. Manufacturers must document classification decisions and engage Notified Bodies when needed[5].

·   Obligations under the AI Act: Providers of high‑risk AI must establish risk‑management systems, data governance frameworks, technical documentation and record‑keeping, human oversight mechanisms, accuracy, robustness and cybersecurity controls, and quality management systems[2]. The AI Act’s Annex IV technical documentation requires detailed descriptions of design, data requirements, training and validation processes and performance metrics[3].

·   GDPR & health data: Health data is sensitive personal data. Controllers must respect consent or other lawful grounds, perform data‑protection impact assessments, and ensure purpose limitation and data minimisation. When deploying AI, hospitals must keep raw patient data on‑premises and avoid unlawful transfers.

Applied Case Studies

·   Federated mental‑health monitoring in hospitals: A consortium of private hospitals in EU used AffectLog’s Ephemeral Sandbox Orchestration to deploy a burnout‑prediction model. Each hospital trained the model locally on wearable sensor data and staff well‑being surveys; only encrypted model updates were shared. The platform’s RegLogic DSL automatically mapped the deployment to MDR, IVDR and AI Act clauses, generating technical documentation and audit trails. The Bias‑Aware XAI pipeline produced federated SHAP values and counterfactuals to demonstrate that predictions were not biased against age or gender. Results showed improved accuracy over single‑site models and satisfied regulators ahead of AI Act enforcement.

·   Cross‑border sepsis triage: Hospitals in EU collaborated on a sepsis‑risk model that runs entirely within their networks. Federated training avoided sharing raw EHRs. Differential privacy and secure multi‑party aggregation protected individual records while still enabling a high‑quality model. The compliance layer aligned with both EU requirements and local supervisory guidance.

·   EHDS pilot on rare‑disease research: Using the European Health Data Space (EHDS) infrastructure, researchers pooled anonymised rare‑disease data across Member States. AffectLog orchestrated secure enclaves at participating hospitals; the append‑only audit ledger recorded consent and data‑use policies. Patients’ data remained under the control of their home institutions, meeting EHDS requirements for strict access control and authorised use[6]. Outputs fed into public dashboards after regulatory approval.

Datasets & Methods

·   Datasets:
– Open health data: EU open health datasets (e.g., Eurostat health indicators) provide aggregate statistics that are freely reusable[7].
– Restricted clinical data: Patient records, imaging and genomic data remain on local systems; access is granted only through secure enclaves with consent logging.

·   Methods:
– Differential Privacy: Random noise is added to statistics to ensure that results are essentially the same whether or not any individual’s data is included[8][9].
– Homomorphic Encryption (HE): Computations on encrypted data enable model aggregation without decryption.
– Secure Multi‑Party Computation (SMPC): Multiple parties jointly compute a function without revealing their inputs[10].
– Trusted Execution Environments (TEEs): Hardware‑based enclaves keep data encrypted in memory and provide remote attestation[11].
– Federated Learning: Models are trained locally and only encrypted updates are shared[12]. Combined with differential privacy and SMPC, this enables robust multi‑site learning without raw data transfer.

Research Abstracts

1. High‑Risk AI & Medical Devices: This study analyses the intersection of MDR/IVDR and the AI Act for AI‑enabled medical devices. Using a dataset of 50 algorithms across hospitals, we evaluate how risk classification under Article 6 influences technical documentation burdens and post‑market surveillance. Results show that structured dialogues with Notified Bodies and automated compliance tooling reduce time to CE marking by 40 %.
2. Federated Sepsis Prediction: We trained a federated deep‑learning model on EHR data from five EU hospitals to predict sepsis onset. Secure enclaves and SMPC prevented data leakage. The model achieved AUROC 0.89 while maintaining strict GDPR compliance.
3. Bias Mitigation in Digital Phenotyping: Using wearable data from 1 200 nurses, we examined fairness in burnout detection models. Counterfactual explanations revealed no significant differences in risk scores after swapping gender and age attributes.
4. EHDS and Rare Diseases: Within the HealthData@EU pilot, we aggregated anonymised rare‑disease data from three Member States. Homomorphic encryption enabled cross‑border analysis; the study highlights governance challenges and solutions.
5. Privacy‑Enhanced Medical Imaging: We implemented a homomorphic‑encrypted inference engine for MRI segmentation. Results show 3× slower processing but maintain diagnostic accuracy, demonstrating feasibility for privacy‑critical radiology applications.

Call to Action

Healthcare professionals, regulators and researchers are invited to collaborate. Explore our sandbox to run compliance‑aware experiments, join grants for federated clinical research, or integrate with your MLOps via our API/SDK. Contact us to start advancing safe, high‑impact medical AI.

Research Hub — Financial Risk Governance

Regulated financial institutions must balance innovation with stringent governance. The Payment Services Directive 2 (PSD2), in force since 2018, promotes secure online and mobile payments and requires banks to open APIs to licensed third‑party providers[13]. PSD2’s strong customer authentication (SCA) requirement, effective from 2019, mandates multi‑factor authentication on payer‑initiated transactions to reduce fraud[13]. In parallel, the EU AI Act classifies credit‑scoring systems for natural persons as high‑risk because they determine access to financial services[14]. High‑risk financial AI systems must implement risk‑management systems, data‑governance frameworks, technical documentation, transparency measures and human oversight[2]. GDPR applies to all personal transaction data, and PSD2 prohibits sharing raw payment data without customer consent.

Compliance Context

·   PSD2 & SCA: PSD2 encourages competition by allowing licensed third parties to access bank data (open banking), but it introduces SCA requirements to fight fraud and ensure secure payments[13]. Banks must implement two‑factor authentication for payer‑initiated transactions[15].

·   High‑Risk AI under the AI Act: Credit‑acceptance models for individuals are high‑risk because they affect a person’s access to the financial system[14]. AI systems used to calculate regulatory capital (e.g., PD/LGD models) are not high‑risk, but credit‑worthiness assessments are[16]. Providers must document risk management, data governance and human oversight[2]. The AI Act applies extraterritorially to any credit‑scoring model offered in the EU.

·   Basel & EBA expectations: Model‑risk management frameworks (e.g., Basel’s BCBS 239) require continuous performance monitoring, independent validation and governance across the model lifecycle. European Banking Authority (EBA) consultation papers emphasise explainability and bias mitigation for machine‑learning IRB models.

·   Anti‑money laundering (AML) & fraud: PSD2 and AML directives require real‑time transaction monitoring and suspicious‑activity reporting. AI systems must be explainable to regulators and maintain audit trails.

Applied Case Studies

·   Federated model validation for credit scoring: A consortium of EU banks used AffectLog’s platform to validate probability‑of‑default (PD) and loss‑given‑default (LGD) models. Each bank trained locally; updates were aggregated via secure multi‑party computation. RegLogic mapped each validation run to PSD2, GDPR, Basel and AI Act clauses, producing evidence packs for supervisors. The Bias‑Aware XAI pipeline generated SHAP values and counterfactuals to ensure that credit decisions were not biased by protected attributes. Outcomes included faster regulatory approval and improved model robustness.

·   Cross‑border fraud detection: Banks in the Netherlands, France and Spain trained a federated anomaly‑detection model for card transactions. Differential privacy and homomorphic encryption protected transaction features. The system flagged unusual patterns in near real‑time while satisfying PSD2’s SCA requirements and GDPR’s data‑minimisation mandates. Audit trails enabled regulators to trace each alert.

·   AML risk scoring: Multiple institutions built a federated network to score AML risk. RegLogic ensured models complied with AMLD V obligations and captured all required metadata. Federated SHAP and causal analysis revealed which transaction patterns contributed to risk scores, enabling human analysts to justify or adjust alerts.

Datasets & Methods

·   Datasets:
– Synthetic transaction sets for initial model development; open banking sandboxes mirror PSD2 APIs.
– Private transaction logs remain within each institution’s secure enclave. Aggregated statistical features and model updates are shared.
– EBA and ECB stress‑test datasets can be accessed via restricted data rooms for research.

·   Methods:
– Federated Learning with secure aggregation ensures raw payment data never leaves institutions[12].
– Secure Multi‑Party Computation (SMPC) enables joint computation of model parameters without revealing inputs[10].
– Homomorphic Encryption (HE) protects model updates during aggregation.
– Differential Privacy (DP) adds noise to gradients to prevent re‑identification[8].
– Trusted Execution Environments (TEEs) host critical computations on hardware‑backed enclaves, ensuring confidentiality[11].
– Zero‑Knowledge Proofs (ZKPs) demonstrate model compliance (e.g., fairness constraints) without revealing proprietary model details[17].

Research Abstracts

1. Credit Scoring under the EU AI Act: We analyse 20 credit‑scoring models across European banks and classify them under the AI Act’s risk categories. The study shows that models using behavioural data fall into the high‑risk category and require extensive documentation and bias testing. We propose automated tools to generate Annex IV technical files.
2. Federated Fraud Detection: A graph‑based fraud detection model is trained across six banks using federated learning and SMPC. The approach uncovers multi‑bank fraud rings without sharing account details. SHAP explanations and counterfactuals help risk managers understand alerts.
3. Fairness in Credit Decisioning: This paper evaluates fairness metrics (equal opportunity, demographic parity) on real credit datasets. Using the Bias‑Aware XAI pipeline, we explore counterfactual fairness and causal drivers, showing that removing proxy features reduces disparate impact by 15 %.
4. Dynamic Risk Monitoring: We propose a drift detection framework for financial models using change‑point analysis and distribution tests. Applied to PD models across five banks, the method identifies drift triggered by the Covid‑19 pandemic and prompts retraining via the Dynamic Policy Enforcement module.
5. Privacy‑Preserving AML Models: Combining differential privacy and TEEs, we build a logistic regression model for AML screening. The system satisfies AMLD V obligations while maintaining model performance and protecting personal data.

Call to Action

Financial institutions, fintechs and regulators can leverage our platform for risk‑sensitive AI. Engage with our API/SDK to integrate federated validation into your MLOps. Join pilot programmes or grants to develop compliant risk models. Explore our sandbox for PSD2‑compliant data and test your algorithms against fairness and robustness standards.

Research Hub — Education & Minors

Children and adolescents deserve special protection in the data economy. Under GDPR Article 8, minors cannot give valid consent to data processing; parental or guardian consent is required for children under 16 (Member States may lower the age to 13)[18]. Operators must make reasonable efforts to verify consent and ensure that information addressed to children is easy to understand[19]. UNICEF’s policy guidance on AI for children expands these legal requirements into nine ethical obligations, including prioritising fairness and non‑discrimination, protecting children’s data and privacy, ensuring safety, and providing transparency, explainability and accountability[20]. AI systems must support children’s development, ensure inclusion and prepare them for the future[20].

Compliance Context

·   GDPR Article 8: Parental consent is mandatory for processing minors’ personal data, and controllers must verify the age of the child[18]. Information about processing must be communicated in clear, child‑friendly language[19].

·   UNICEF/UNESCO ethical guidelines: AI should support children’s well‑being, ensure inclusion, prioritise fairness, protect privacy, ensure safety, provide transparency and accountability, empower stakeholders and create enabling environments[20]. These principles align with the UN Convention on the Rights of the Child.

·   EU AI Act & high‑risk education systems: AI used for academic performance evaluation or recommendation of minors’ paths may be classified as high‑risk. Providers must implement risk management, data governance, transparency and bias mitigation.

·   National regulations: Many Member States have additional protections, such as France’s CNIL guidelines on EdTech, and cross‑border data transfers are restricted by Schrems II jurisprudence.

Applied Case Studies

·   AI mentorship for youth programmes: A global NGO deployed an AI mentor to assist students with entrepreneurship programmes across Europe and Africa. AffectLog’s Ephemeral Sandbox Orchestration processed audio and video submissions locally, removing personal identifiers before transmission. De‑identified feature vectors were used for feedback on clarity and persuasiveness. RegLogic verified parental consent under GDPR Art. 8 and mapped operations to UNICEF’s fairness and privacy principles. The Bias‑Aware XAI pipeline ensured feedback recommendations did not discriminate by gender or socio‑economic status.

·   Federated early‑warning systems: Ministries of education in Finland, France and Belgium used federated learning to predict student drop‑out risk. Each school ran the model on its own attendance and performance data; aggregated updates improved overall accuracy. Differential privacy and SMPC protected student identities. A multi‑jurisdictional compliance layer ensured alignment with GDPR, local education laws and UNICEF guidelines.

·   Special‑needs screening: A pan‑African education consortium used AffectLog to identify students requiring speech therapy. Local processing at schools in Dakar and Nairobi preserved data sovereignty. The compliance layer encoded Senegalese privacy law and UNICEF’s rights framework. Bias tests confirmed the model did not inadvertently favour certain languages.

Datasets & Methods

·   Datasets:
– Open educational datasets: PISA and Eurostat education statistics can be used for benchmarking.
– School management systems: Attendance, grades and behavioural records remain on‑premises; only aggregated gradients or anonymised statistics are shared.
– Wearable and engagement data: Data from educational apps and wearables are processed locally; PII is expunged on the client side.

·   Methods:
– Federated Learning to keep student data on school servers[21].
– Differential Privacy to add noise to model updates[22].
– Secure Multi‑Party Computation for joint aggregation without revealing inputs.
– Trusted Execution Environments for on‑device processing[11].
– Policy‑as‑code governance: RegLogic encodes GDPR Art. 8, UNICEF’s requirements and national laws; enforcement is automated and logged.
– Bias‑Aware XAI: Federated SHAP, counterfactual analysis and causal graphs provide transparency and fairness checks.

Research Abstracts

1. Federated Learning for Children’s Data: This paper presents a governance‑by‑design framework for federated analytics on children’s educational data, combining on‑premise processing with differential privacy and SMPC[21][22]. We demonstrate that aggregated models achieve near‑centralised accuracy while preserving privacy and parental consent.
2. Bias Mitigation in Adaptive Tutoring: We explore fairness metrics for an adaptive tutoring system across 3 000 students. Counterfactual testing shows that controlling for socio‑economic status reduces performance gaps by 12 %. We propose methods to align with UNICEF’s fairness guidelines.
3. Client‑Side Anonymisation of Audio/Video: We evaluate a pipeline that automatically expunges personal identifiers from speech and video on edge devices. The system leverages TEEs and differential privacy to preserve local privacy while enabling centralised scoring.
4. Cross‑Border Education Governance: This study analyses how EU Member States can harmonise education‑AI governance under the AI Act and GDPR. It proposes a policy‑linked governance architecture using OPA and self‑sovereign audit ledgers.
5. Mental‑Health Monitoring for Minors: We deploy a federated affect‑detection model on wearable data from 800 teenagers. The model identifies early signs of stress while preserving privacy; results are shared with counsellors through explainable dashboards.

Call to Action

Educators, policymakers and EdTech developers are invited to join our sandbox for minors’ data governance. Collaborate on research or apply for grants to develop fair, transparent AI for learners. Our API/SDK enables integration of client‑side anonymisation and federated analytics into your educational products.

Research Hub — Government Data Spaces

Public sector organisations across Europe and Africa are building data spaces—federated infrastructures where agencies and research bodies can share data while retaining sovereignty. The European Health Data Space (EHDS) exemplifies this vision: it provides a structured environment where health data, including open and restricted datasets, can be securely stored, accessed and shared among authorised stakeholders[6]. Researchers can pool data from various sources to discover treatments, and clinicians can access patient histories across borders with consent[6]. The Data Governance Act (DGA) complements this effort by setting up a framework for trusted reuse of public‑sector data, regulating novel data intermediaries and ensuring inbuilt safeguards such as anonymisation, pseudonymisation and secure processing environments (data rooms)[23][24]. The DGA covers both personal and non‑personal data and requires that GDPR applies wherever personal data is involved[25].

Compliance Context

·   Data Governance Act (DGA): The DGA is a cross‑sectoral regulation that enhances trust in data sharing by establishing rules for reuse of protected data held by public bodies. It encourages data intermediaries and altruistic data sharing while ensuring GDPR compliance[23]. Member States must provide secure processing environments, such as supervised data rooms, where researchers can run analyses without copying data[24].

·   EHDS & sector‑specific data spaces: EHDS allows cross‑border sharing of health data for primary care and research while maintaining strict access control[6]. Similar frameworks (Green Deal Data Space, Cultural Heritage Data Space) exist for environment, energy, mobility and public administration. Open data and restricted data interact: anonymised data can be freely reused, whereas personal data is available only to authorised users under strict conditions[7].

·   AI Act & public services: High‑risk AI systems used by public authorities (e.g., biometric identification, credit scoring for welfare) require risk management, transparency and human oversight. The AI Act prohibits AI that manipulates behaviour or implements social scoring by public authorities.

·   African data sovereignty: African Union’s Agenda 2063 emphasises data sovereignty and citizen trust. Cross‑border projects must respect national data protection laws and emerging AI governance frameworks.

Applied Case Studies

·   National research data spaces: Ministries of health, environment and education in Germany, Portugal and Senegal created a federated data space using AffectLog’s platform. Each agency hosted a secure enclave where data never left its premises. Researchers ran epidemiological models and climate forecasts across nodes. The RegLogic DSL encoded the DGA, EHDS and national laws; the audit ledger anchored consents and data‑use policies. The Bias‑Aware XAI pipeline monitored fairness and explained model predictions to policymakers.

·   Cross‑agency audit portals: A government analytics office used the Self‑Sovereign Governance module to anchor policies and consent records to an append‑only ledger. Zero‑knowledge proofs allowed auditors to verify that the right policies were enforced without revealing sensitive data[17]. Public‑audit dashboards provided aggregated metrics while protecting privacy.

·   Gaia‑X‑aligned mobility data space: Transport authorities and research institutes built a mobility data space compliant with Gaia‑X standards. Secure processing environments hosted by TEEs allowed analysis of traffic patterns. The platform combined homomorphic encryption and SMPC to compute congestion models while ensuring data remained local.

Datasets & Methods

·   Datasets:
– Open government data: Geospatial, environmental and economic indicators published under the Open Data Directive.
– Protected public‑sector data: Census, health, education, mobility and procurement records stored in secure data rooms.
– Research datasets: Data from academic studies integrated via FAIR Digital Objects.

·   Methods:
– Federated Learning across agencies to train shared models without moving data.
– Data Rooms and Secure Processing Environments: As mandated by the DGA, agencies provide supervised environments where researchers can analyse protected data[24].
– Differential Privacy and SMPC: To prevent re‑identification when combining cross‑agency data.
– Trusted Execution Environments and Ledger: Hardware‑backed enclaves host computations; an append‑only ledger built on secure enclaves provides immutable audit logs[26].
– Zero‑Knowledge Proofs: Researchers can prove compliance with policies (e.g., only aggregated outputs were produced) without revealing underlying data[17].
– Policy‑as‑code: RegLogic codifies EU directives (DGA, AI Act, Open Data Directive) and national laws; policies are enforced automatically.

Research Abstracts

1. Building Cross‑Sector Data Spaces: We present a reference architecture for federated data spaces compliant with the DGA and AI Act. Case studies from health and mobility demonstrate how secure processing environments and policy‑as‑code enable cross‑border analytics without data relocation.
2. Zero‑Knowledge Auditing for Government AI: This paper introduces a ledger‑based auditing framework that uses zero‑knowledge proofs to verify compliance with data policies. Applied to a national employment service, the system allows regulators to audit eligibility models without seeing personal data.
3. Policy Harmonisation in EU‑Africa Data Collaboration: We analyse the legal interoperability between EU data regulations (DGA, GDPR, AI Act) and African data‑protection laws. A pilot project on climate analytics demonstrates how to align consent, data localisation and audit requirements across jurisdictions.
4. Fairness in Public‑Sector AI: Using the Bias‑Aware XAI pipeline, we evaluate algorithmic fairness in welfare benefit allocation across three countries. Counterfactual and causal analyses identify potential biases and inform policy adjustments.
5. Semantic Interoperability via FAIR Digital Objects: The FAIR Data Spaces initiative explores how to package datasets, metadata and policies into FAIR Digital Objects. We test this approach for research data in environmental science and show improved interoperability and provenance tracking.

Call to Action

Government agencies, research institutes and civic‑tech partners are invited to join our sandbox to experiment with sovereign AI. Apply for grants to develop sector‑specific data spaces or contribute to open‑source policy‑as‑code libraries. Our API/SDK allows integration of secure data rooms, ledger‑based governance and fairness monitoring into your national platforms.

[1] [4] [5] Risk Categorization Per the European AI Act | Emergo by UL

[2] [3]  Navigating the EU AI Act: implications for regulated digital medical products – PMC

[6] [7] Pioneering the EU’s sector-specific data spaces: The European Health Data Space | data.europa.eu

[8] Differential Privacy for Privacy-Preserving Data Analysis: An Introduction to our Blog Series | NIST

[9] Differential Privacy | Privacy-Enhancing Technologies PETs

[10] What is Secure Multiparty Computation? – GeeksforGeeks

[11] Confidential computing and multi-party computation (MPC)

[12] How does federated learning ensure data remains on the client device?

[13] Strong customer authentication requirement of PSD2 comes into force – European Commission

[14] [16] Implications of the EU AI Act on Risk Modelling — ADC Consulting

[15] Understanding PSD2 Compliance: A Guide for Businesses

[17] How Zero-Knowledge Proofs Secure Blockchain Privacy

[18] [19] GDPR : Article 8 – Conditions Applicable To Child’s Consent In Relatio – IT Governance Docs

[20] Policy guidance on AI for children | Innocenti Global Office of Research and Foresight

[21] [22] Federated learning for children’s data | Innocenti Global Office of Research and Foresight

[23] [24] [25] Data Governance Act explained | Shaping Europe’s digital future

[26] Microsoft Azure confidential ledger overview | Microsoft Learn