What Is Synthetic Data?
Synthetic data is artificially generated data that replicates the statistical properties, patterns, and structure of real-world data without containing actual personal information or sensitive records. It is created using AI techniques (generative models, statistical sampling, simulation) to produce datasets that are useful for AI training, testing, and development when real data is insufficient, too sensitive, or legally restricted. Synthetic data is used to augment small training datasets, protect privacy by avoiding the use of real personal data, create balanced datasets to reduce bias, and test AI systems against edge cases that are rare in real-world data.
Why it matters for governance
Synthetic data is not automatically safe or bias-free. If the generation process is based on biased real-world data, the synthetic data will reproduce those biases. Re-identification risk exists if synthetic records can be linked back to real individuals (especially in small populations). Regulatory acceptance varies — some regulators accept synthetic data for compliance testing, others do not. Model performance on synthetic data may not reflect real-world performance. Governance should address the provenance and quality of the source data used to generate synthetic datasets, re-identification risk assessment, fairness testing of synthetic data across demographic groups, and documentation of the generation methodology.