Synthetic Data Is Here to Stay, but How Secure Is It?

**Executive Summary:** This commentary explains that regulatory pressure (notably GDPR) and rising privacy expectations limit access to real datasets, and presents synthetic data as a practical, privacy-preserving supplement for AI training while highlighting risks such as re-identification and model collapse; it recommends best practices including outlier handling, automated risk assessment, and secure deletion of original source data to balance innovation with compliance.