A legal brief frames synthetic data as a practical way to reduce GDPR exposure while keeping datasets usable for analytics and model training. The catch: teams still need to prove privacy claims with risk testing and documentation, not assumptions about “anonymization.”
Legal brief: Synthetic data can ease GDPR risk—if you can validate privacy and utility
A Journal of Technology Law & Policy article argues that synthetic data—AI-generated data designed to preserve the statistical properties of a source dataset—can help organizations navigate the “privacy-utility tradeoff” under the EU’s GDPR. The piece positions synthetic data as an alternative to sharing or training on raw personal data, and contrasts it with pseudonymized data, which still retains a link (direct or indirect) to individuals.
The article also highlights the incentives for large technology companies to adopt synthetic data approaches: they can continue to extract value from data (including for advertising-driven business models) while reducing privacy exposure. The core claim is not that synthetic data automatically solves GDPR, but that it can be a meaningful tool for minimizing personal data processing—provided the synthetic generation process and resulting datasets are handled with care.
- Lower exposure than raw or pseudonymized data—sometimes. For data teams, synthetic datasets can enable broader internal sharing and model development with reduced reliance on direct identifiers. But GDPR posture depends on whether outputs are truly anonymous in context, not on the label “synthetic.”
- Privacy engineering work doesn’t disappear. If you use synthetic data to justify wider access or external sharing, you still need re-identification and leakage risk validation, plus clear documentation of assumptions, threat models, and test results.
- Governance becomes the product. The operational differentiator is the end-to-end process: generation method selection (e.g., GAN-based approaches noted in the brief), evaluation of utility vs. privacy, and controls around who can regenerate or link back to source data.
- Procurement and compliance will ask for evidence. Expect DPIA-style questions: what personal data touched the pipeline, what metrics were used to assess privacy risk, and what residual risks remain—especially when synthetic data is used for ad targeting, measurement, or model training tied to user behavior.