Multi-Country Governance: One Model, Many Legal Contexts
Deploying a single artificial intelligence system across multiple European jurisdictions presents a paradox: the technology is unified, but the legal obligations are fragmented. A model trained once, hosted centrally, and accessed via a common API must simultaneously comply with the GDPR in France, the BDSG in Germany, the DPA in the Netherlands, and the forthcoming AI Act obligations across all 27 Member States. For professionals in AI, robotics, biotech, and public administration, the challenge is not merely technical; it is architectural. It requires a governance model that is resilient enough to withstand local legal variance without becoming a labyrinth of bespoke exceptions. This article examines how to design and operate such a system, focusing on governance structures, the definition of local roles, and the mechanics of escalation when legal interpretations diverge.
The Principle of “One System, Many Contexts”
At the heart of multi-country AI governance is the distinction between the system architecture and the regulatory perimeter. The system architecture refers to the compute, data pipelines, model weights, and user interfaces that are logically centralized or distributed. The regulatory perimeter, however, is defined by where the data subjects are located, where the controller or processor is established, and where the “significant effects” of an AI system are felt. Under the GDPR, the “one-stop-shop” mechanism aims to simplify cross-border processing, but it does not eliminate the need to respect the national laws that supplement the GDPR. Similarly, the AI Act establishes a harmonized framework, but Member States retain significant discretion in enforcement, market surveillance, and the designation of notifying authorities.
Therefore, a “one model” approach must be built on a governance framework that treats legal compliance as a set of configurable parameters rather than a static codebase. This means designing the system to be aware of its legal context. For example, a biometric identification system deployed in a retail environment in Sweden faces different national prohibitions and ethical guidelines than the same system deployed in a security context in Poland. The model weights may be identical, but the deployment configuration must be jurisdiction-specific.
Legal Fragmentation in the European Union
While the EU aims for a Digital Single Market, legal fragmentation persists in three key areas relevant to AI: data protection, sector-specific regulations, and consumer protection.
Data Protection and the Role of National Laws
The GDPR is a regulation directly applicable in all Member States, but it includes opening clauses that allow Member States to legislate on specific processing activities. For instance, Article 88 of the GDPR allows Member States to provide more specific rules for the processing of employee data. This creates immediate divergence. An AI system used for HR analytics must adhere to the German Betriebsrat (works council) consultation requirements, which are far more stringent than the general information obligations under the GDPR. In contrast, in the UK (post-Brexit), the UK GDPR applies, creating a distinct regulatory border for data flows.
Furthermore, the concept of “legitimate interest” as a basis for processing is interpreted differently. The French CNIL has historically taken a strict view on profiling and automated decision-making, whereas the Irish DPC might offer a more business-friendly interpretation regarding data minimization in the context of large language models. A robust governance model must map these interpretations to specific data processing activities.
AI Act: Harmonization vs. National Enforcement
The AI Act introduces a Union-wide legislation, but its implementation relies heavily on national authorities. Member States must designate market surveillance authorities and notifying authorities. The European AI Office will coordinate, but the “regulatory sandboxes” and real-world testing environments are managed nationally. This means that a high-risk AI system in the healthcare sector (e.g., a diagnostic tool) will undergo conformity assessments that, while based on the same standards, may be conducted by different bodies with different procedural nuances in Germany (TÜV) versus Italy (Ministero della Salute).
Crucially, the AI Act prohibits certain practices (e.g., emotion recognition in the workplace) but allows exceptions for security or medical purposes. The interpretation of “medical purposes” can vary. Is a wellness app that monitors stress levels a medical device? In France, it might require CE marking under the MDR; in Estonia, it might be classified as a general wellness product with lighter oversight.
Governance Design: The Hub-and-Spoke Model
To manage this fragmentation, organizations typically adopt a Hub-and-Spoke governance model. The “Hub” represents the central AI development and operations team, responsible for the core model, the data infrastructure, and the baseline ethical principles. The “Spokes” represent local entities—subsidiaries, legal representatives, or Data Protection Officers (DPOs)—responsible for local adaptation and compliance.
This model is not merely organizational; it is technical. The Hub must engineer the system to allow for “jurisdictional gates.” These are technical checkpoints that trigger specific legal workflows based on the user’s location or the nature of the data being processed.
The Central Governance Layer (The Hub)
The Hub is responsible for the Model Risk Management (MRM) framework. This includes:
- Algorithmic Impact Assessments (AIA): Conducting a baseline assessment of the model’s risks (bias, robustness, explainability). This assessment serves as a template but must be supplemented by local risk factors.
- Data Provenance: Ensuring that the training data, even if aggregated from multiple countries, respects the original collection constraints. If data was collected in Germany under the BDSG, it cannot be “re-purposed” for a different legal basis without a new assessment.
- Standard Operating Procedures (SOPs): Creating the “Golden Source” documentation for the AI system, including technical specifications, intended purpose, and risk mitigation measures.
The Local Governance Layer (The Spokes)
The Spokes are the eyes and ears of the Hub in the local legal environment. Their role is to validate the Hub’s assumptions against local reality.
The Local Legal Representative
Under the AI Act, providers established outside the EU must appoint an authorized representative in the Union. Even for EU-based providers, having a local legal representative in high-risk markets is prudent. This representative acts as the addressee for local authorities. They do not merely forward letters; they must have the authority to stop the deployment locally if legal risks materialize.
The Local DPO and Ethics Board
In many European countries, the DPO is a statutory role with specific independence requirements (e.g., in Germany, the DPO cannot be dismissed for performing their duties). The local DPO must review the Hub’s Data Protection Impact Assessment (DPIA) and add a “local addendum.” For example, while the Hub might view “legitimate interest” as sufficient for processing, the local DPO in France might insist on explicit consent due to the CNIL’s guidelines on sensitive data.
Some countries, like France and Germany, have strong traditions of works council involvement. The governance design must explicitly include the works council in the “Spoke” layer. They must be consulted on the introduction of AI tools that monitor employees, and their agreement (or lack thereof) can effectively veto deployment in that jurisdiction.
Operationalizing Local Roles
Clear role definitions are the antidote to regulatory paralysis. In a multi-country deployment, ambiguity regarding who makes the final call on compliance leads to either “lowest common denominator” compliance (restricting the system everywhere to satisfy the strictest country) or “regulatory arbitrage” (ignoring local nuances).
Defining the “Controller” and “Processor”
Under GDPR, the distinction between Controller and Processor determines liability. In a multi-country AI system, the Hub is often the Processor (hosting the model), while the local entity using the AI is the Controller (determining the purpose). However, in cases where the Hub uses the data to retrain the model, the roles blur. The Hub becomes a Controller for the retraining activity.
A common failure mode is assuming the Hub is the sole Controller. This exposes the local entity to risk if they are deemed to be exercising “decisive influence” over the processing without fulfilling Controller obligations. The governance contract must clearly delineate these roles.
The “Super-User” Role: The Compliance Architect
Between the Hub and the Spokes, we recommend a specialized role: the Compliance Architect. This is a technical-legal hybrid role (often found in larger organizations as a “Privacy Engineer”). The Compliance Architect translates legal requirements into system configurations.
Example: The Italian Garante requires specific transparency information for automated decision-making. The Compliance Architect ensures that the API response from the AI system includes a specific JSON payload containing the logic involved, which the Italian front-end application renders. The German market might require a different format or deeper explainability. The Compliance Architect manages these forks in the codebase.
Escalation Paths
When a local regulator issues an inquiry or a new interpretation of the law, the escalation path must be immediate and documented.
- Detection: The Local DPO or Legal Rep receives the signal.
- Assessment: Immediate impact analysis: Does this affect the “one model” architecture? Does it require a local fork?
- Notification: The Hub’s Chief Compliance Officer and Data Protection Officer are notified within 24 hours.
- Decision: The Hub decides whether to:
- Globalize: Apply the stricter local rule to all users (e.g., disabling a feature globally).
- Localize: Implement a technical switch to disable the feature only in that jurisdiction.
- Litigate/Challenge: If the interpretation is deemed incorrect, engage in dialogue with the regulator (via the Local Rep).
Technical Implementation of Governance
Governance is often discussed in abstract terms, but in AI systems, it must be encoded. We advocate for Compliance-as-Code principles, where legal rules are translated into system logic.
Geofencing and Jurisdictional Awareness
The system must know where the user is. This goes beyond IP geolocation. It requires a robust identity verification layer that ties the user to a jurisdiction. Once the jurisdiction is established, the system should load the corresponding “Legal Profile.”
Legal Profile: A set of configuration parameters that dictate system behavior.
- Profile: DE (Germany)
- Strictness Level: High
- Automated Decision Making: Disabled or requires human review
- Biometric Processing: Prohibited (unless specific security exemption)
- Retention Period: 30 days (BDSG standard)
- Profile: FR (France)
- Strictness Level: High
- Automated Decision Making: Allowed with strict transparency
- Biometric Processing: Allowed for security with CNIL authorization
- Retention Period: Varies by data type
Dynamic Consent Management
Consent must be granular and revocable. In a multi-country context, the “withdrawal of consent” must propagate instantly across the entire system. If a user in Spain withdraws consent for training data usage, the Hub must ensure that their data is excluded from the next training cycle. This requires a centralized “Consent Ledger” that is queried by the training pipeline.
Audit Trails and Logging
Regulators will ask for logs. The logging mechanism must be capable of segregating logs by jurisdiction to facilitate audits by local authorities. If the Dutch DPA investigates a specific transaction, the system must be able to produce the relevant logs without exposing data from other jurisdictions, respecting data minimization.
Comparative Analysis: The Divergence of National Implementations
To illustrate the practical necessity of this governance model, we examine three distinct regulatory environments.
Germany: The Works Council and Data Minimization
Germany represents the pinnacle of strict data protection enforcement, heavily influenced by the Federal Constitutional Court’s right to informational self-determination. The Betriebsrat (Works Council) has a veto right over the introduction of AI that monitors employees.
Practical Implication: A US-based company deploying a centralized productivity AI tool cannot simply roll it out in its German office. The governance model requires the German “Spoke” to engage the Works Council months in advance. If the Works Council objects, the Hub must either disable the tool for German employees or redesign it to remove the monitoring features. The “one model” approach survives only if the model allows for feature toggling based on local labor agreements.
France: The CNIL and “Privacy by Design”
France’s CNIL is proactive and issues detailed guidelines (e.g., on biometrics, on cookies). They emphasize Privacy by Design. In the context of AI, the CNIL has been aggressive about “scraping” data for training.
Practical Implication: If the Hub trains a model on data scraped from the French web, the French “Spoke” must verify that the scraping respected the robots.txt standard and did not collect sensitive data (health, religion). If the CNIL issues a fine, the liability usually falls on the entity established in France. Therefore, the French subsidiary must have the authority to audit the training data used by the Hub, even if the Hub is in Ireland.
Spain: The AEPD and Consumer Protection
The Spanish AEPD (Agencia Española de Protección de Datos) is very active in consumer-facing AI. They focus heavily on transparency and the rights of consumers under the General Data Protection Regulation (GDPR) and the AI Act.
Practical Implication: For an AI system used in e-commerce (e.g., dynamic pricing), the Spanish “Spoke” must ensure that the system does not engage in discriminatory pricing based on protected characteristics. The governance model requires the local compliance team to run regular “fairness audits” on the output of the pricing model, specifically looking for biases that might be acceptable in other markets but illegal in Spain.
Managing Cross-Border Data Transfers
A major friction point in “One Model” architectures is the movement of data between the EU and third countries (e.g., the US). The Schrems II judgment and the subsequent Data Privacy Framework (DPF) have created a volatile legal landscape.
The Role of Standard Contractual Clauses (SCCs)
Most AI providers rely on SCCs to transfer personal data outside the EU. However, SCCs are a contract, not a magic wand. They require a “Transfer Impact Assessment” (TIA).
The Hub must conduct a TIA for the destination country. If the Hub is in the US, it must assess whether US surveillance laws (FISA 702) undermine the protections promised in the SCCs. The local “Spoke” (the EU entity) is responsible for verifying that the TIA is robust. If the local Spoke believes the transfer is illegal, they must stop the flow of data.
Technical Measures: Pseudonymization and Encryption
To mitigate risks, the Hub should implement technical measures such as:
- Pseudonymization at the source: Data is stripped of direct identifiers before leaving the EU.
- Homomorphic Encryption (where feasible): Allowing the model to process data without “seeing” the raw inputs.
- Edge Processing: Keeping sensitive processing (e.g., biometric matching) on local servers within the jurisdiction, sending only non-sensitive metadata to the central Hub.
Escalation Mechanisms in Practice
When a regulatory conflict arises, the governance model must provide a clear escalation ladder.
Scenario: The “High-Risk” Classification Dispute
Imagine an AI system for sorting medical X-rays. The Hub classifies it as a Class IIa medical device under the MDR. However, the Italian Competent Authority (Ministero della Salute) flags it as Class III due to the critical nature of the diagnosis.
Escalation:
- The Italian “Spoke” receives the notification.
- The Local Regulatory Affairs officer alerts the Hub’s Regulatory Lead.
- The Hub’s Legal Counsel reviews the classification rules.
- Decision Point: The Hub must decide whether to challenge the Italian assessment or upgrade the compliance documentation globally to Class III standards.
- Operational Impact: If upgraded, the system must be re-certified, affecting deployment timelines in all other countries.
Scenario: The “Prohibited Practice” Inquiry
A Dutch regulator (AP) investigates a “social scoring” AI used by a local municipality. The Hub argues it is a “reliability score” for administrative efficiency, not a punitive social score.
Escalation:
- The Dutch “Spoke” manages the inquiry.
- The Hub provides technical documentation proving the model’s output is not used for punitive measures.
- If the AP disagrees, the “Spoke” must immediately suspend the system in the Netherlands to avoid fines.
- The Hub reviews the “Prohibited Practice” definition in the AI Act to see if a global modification is required to prevent similar interpretations in other countries.