Synthetic data’s next phase: scaling training, proving safety, and tightening governance

Three new reads highlight synthetic data’s expanding role: scaling AI training when real data and labels are scarce, evaluating healthcare-specific applications and risks, and outlining a legal framework for when synthetic data reduces privacy exposure versus when it introduces new harms. The common thread is that synthetic data’s value is increasingly operational—but only if teams can prove quality, manage bias, and document safeguards.