IEEE published a study positioning synthetic data as a practical tool for reducing exposure to sensitive personal data while supporting AI development under tightening privacy regulation. For data and compliance teams, the message is less about novelty and more about operationalizing synthetic data in regulated workflows.
IEEE: Synthetic data can reduce privacy risk without halting AI work
On Nov 10, 2025, IEEE published a study examining synthetic data’s role in AI privacy and regulatory compliance, with particular relevance to sensitive domains like healthcare and finance. The study frames synthetic data as a way to generate realistic datasets that preserve statistical utility for training and analytics while limiting direct use of real personally identifiable information (PII).
IEEE also calls out common generation approaches, including Generative Adversarial Networks (GANs), as part of the technical toolkit for producing high-fidelity synthetic datasets. The report ties adoption to regulatory pressure, explicitly referencing GDPR and CCPA as drivers pushing organizations toward approaches that lower exposure to real data in model development and analysis pipelines.
- Compliance posture: If synthetic data is used to reduce reliance on real PII, it can support GDPR/CCPA-aligned programs by narrowing where sensitive data is processed and who can access it.
- Breach and audit surface area: Replacing some training/analytics datasets with synthetic alternatives can reduce the blast radius of leaks and simplify controls around data sharing, vendor access, and sandbox environments.
- Engineering reality check: Methods like GANs can improve fidelity, but teams still need validation gates (utility, bias, and privacy leakage testing) to avoid “synthetic” becoming a false sense of safety.