Define the use before generating
Synthetic data for load testing needs different realism than synthetic data for model training. Decide the fields, ranges, relationships, and failure cases the data must preserve. Do not generate a large dataset just because the tool can.
A safer process
- Remove unnecessary personal fields from the seed data.
- Generate in a controlled environment with access logs.
- Test for copied values, rare combinations, and re-identification risk.
- Compare distributions, correlations, and important subgroups with real data.
- Keep synthetic data separate from the protected evaluation set.
- Document how it was generated and where it must not be used.
The realism trap
Data can look realistic while missing the rare cases that break a system. Add deliberate edge cases and have a domain expert review whether the generated examples describe the real process rather than a model’s stereotype of it.