Data sovereignty is the idea that personal data about people in a jurisdiction should be subject to that jurisdiction’s laws and controls. For Europe, this means that the GDPR and national data protection authorities have the final say over how personal data is used and transferred outside the EU. For LLM builders, data sovereignty raises three immediate questions. Can training data that contains personal data be lawfully processed? Can model training happen abroad? And if it does, what legal and technical measures are required so the transfer complies with EU rules? Let’s discuss.
The core legal constraints in plain language
First, the GDPR still governs model training when training datasets contain personal data. Controllers must identify a lawful basis to process personal data. Consent is one option. Legitimate interest is another basis, but it requires a balancing test and careful documentation. Controllers must also respect special category data rules, minimisation and the rights of data subjects. Refer to Article 6 and the EDPB guidance for guidance on how these lawful bases are applied in practice.
Second, cross-border transfers remain tightly constrained after the CJEU’s Schrems II ruling. Exporting personal data to jurisdictions without an EU-equivalent level of protection is allowed only with appropriate transfer tools plus “supplementary measures.” The EDPB has issued detailed guidance and, in 2025, published final guidance clarifying how to evaluate third-country access by public authorities and what technical and contractual measures are robust enough to reduce legal risk. For large model training pipelines that span cloud regions or global suppliers, these rules are central.
Third, the upcoming and now partly enforced AI Act imposes additional data quality and dataset obligations on top of data protection requirements for high-risk systems. Article 10 requires developers of high-risk systems to use quality, representative and well-documented datasets for training and validation. This creates overlap between data governance (GDPR) and model governance (AI Act), requiring compliance teams to coordinate on both fronts.
Enforcement and the real world
National DPAs have taken concrete action against generative AI services. Italy’s Garante fined OpenAI 15 million euros for breaches tied to data use and transparency. That case demonstrates that regulators will utilise existing data protection tools to challenge opaque training practices. Meanwhile, courts and rights holders are also litigating copyright and data-use disputes that could constrain how datasets are compiled and reused. These enforcement moves change the economics of global training at scale.
Why anonymisation is not the simple escape
Many engineers assume that anonymising data fixes the legal problem. In practice, proper anonymisation is hard to prove at scale. Pseudonymization helps reduce risk, but pseudonymized data still counts as personal data under the GDPR if re-identification is reasonably possible. Re-identification risk increases when datasets are combined or when models can memorise and reproduce verbatim fragments. That means legal teams and builders must treat anonymisation claims with caution and require technical proofs, not just assertions.
Technical strategies that reduce legal friction
There are technical patterns that meaningfully reduce transfer and sovereignty risk. None is perfect, but combined, they change the compliance calculus.
• Federated learning. Train models at the edge using local datasets and aggregate model updates centrally, without transferring raw personal data. This reduces cross-border transfers of plain data, but it still raises questions about whether update vectors leak personal information and whether the aggregation server is subject to foreign access.
• Differential privacy. Inject controlled noise into gradients so the model cannot reveal specific training records. Properly tuned, differential privacy limits memorisation and strengthens anonymisation claims. Implementation complexity is non-trivial, and it often reduces the utility of the model.
• Synthetic data. Replace or augment personal data with synthetic analogues generated under strict constraints. Synthetic datasets are helpful for testing and pretraining, but regulators will require provenance and validation to ensure that the synthetic data does not contain real individuals.
• Secure enclaves and confidential computing. Use Trusted Execution Environments to keep raw data and code inside encrypted hardware zones. These reduce exposure to third-country access, but the jurisdictions of cloud providers and the potential for compelled access remain legal questions.
• Localised training and on-prem options. Where sovereignty is non-negotiable, train models inside EU data centres or on customer premises. This increases cost, but it provides the most direct compliance posture.
Operational and contractual controls you must have
Legal and product teams must coordinate on at least these actions.
- Conduct a thorough Data Protection Impact Assessment for training pipelines that use personal data. The EDPB and many DPAs expect DPIAs when processing is high-risk.
- Map data flows precisely. Which datasets, annotations, or scraped texts contain personal data? Where do they reside before, during and after training?
- Use approved transfer mechanisms, along with supplementary measures. Standard Contractual Clauses remain widely used, but they now require a transfer impact assessment and technical/organisational safeguards that demonstrably mitigate third-country access. Keep records of the evaluation.
- Embed contractual protections with cloud and annotation vendors. Contracts must specify data locality, security controls, audit rights and how requests from foreign authorities will be handled.
- Maintain transparency for data subjects. Update privacy notices, publish datasets’ provenance statements, and offer clear channels for rights requests. Regulators now expect concrete explainability about how personal data contributed to model outputs. CNIL and the ICO have published practical guidance for AI projects that emphasise transparency in annotation and pipeline practices.
Risk scenarios to watch
Training on scraped web data that contains personal profiles remains one of the most sensitive areas for European LLM developers, as even publicly available information can trigger GDPR obligations if it pertains to identifiable EU subjects. The legal risk grows when companies rely on US-based cloud regions without robust supplementary measures, since this raises familiar Schrems II concerns about third-country access. The picture gets even more complicated when annotation work is outsourced to jurisdictions with weak contractual or technical safeguards. Several recent enforcement actions show that data protection authorities are actively investigating dataset provenance, annotation pipelines and the conditions under which personal data crosses borders.
Risk Scenarios Developers Must Keep in View
European LLM teams face a cluster of legal hazards that often hide inside everyday engineering decisions. The first is training on scraped web data that includes personal profiles. Even when the material is publicly available, GDPR obligations still apply if a European data subject can be identified. The second risk comes from using US-based cloud regions without adequate supplementary measures, which can trigger the same Schrems II concerns that have already disrupted other cross-border data transfers. The third involves outsourcing annotation to vendors in countries with weak privacy laws and regulations. Regulators have shown that they will examine dataset provenance, annotation practices and the potential for foreign access. These three scenarios appear simple on the surface, but together they define the core legal exposure for any model trained on mixed global data.
Big picture trade-offs
Localised training increases cost and slows iteration. Heavy technical anonymisation can reduce model quality. Aggressive global pipelines cut costs and speed, but raise regulatory exposure. The right balance depends on a business’s risk tolerance, customer expectations, and the sectors the model will serve. For regulated sectors such as health or finance, the bar is considerably higher.
Conclusion
For European LLM projects, data sovereignty is not a checkbox. It is an organisational constraint that reshapes architecture, procurement and product strategy. The only defensible path is a mix of technical safeguards, rigorous impact assessments, contractual discipline and operational transparency. Companies that treat these steps as engineering requirements rather than legal afterthoughts will be the ones that build models Europe can both trust and use.
