Synthetic data gets a governance upgrade: EU guidance, global health proof points, and vendor playbooks

EDPS published guidance positioning synthetic data as a privacy-relevant tool for ML while warning about outliers, bias transfer, and the need for quality control. NIH highlighted synthetic data for safer medical data sharing in low-resource settings (including Kenya), citing GAN approaches like CTGAN. NVIDIA outlined synthetic data and simulation workflows to cover data gaps and rare corner cases, and a legal-focused blog argued synthetic data can reduce privacy, IP, and bias risks when paired with stronger governance.