Testing an AI system well means feeding it realistic data. Doing that with real customer records pulls personal data into development, testing and demonstration environments, each of which widens exposure under UK GDPR.
Synthetic data offers another route. It is artificially generated to reproduce the statistical shape of a real dataset, the distributions and edge cases a model needs to see, without copying real individuals into the output. Teams can build, test and demonstrate systems while live personal data stays in its controlled environment.
The Caveat That Matters
Synthetic data is not automatically outside data protection law. If a generated record can be traced back to a real person, or if the generator reproduces real examples it has memorised, the privacy benefit is lost. The Information Commissioner's Office treats synthetic data as one of a set of privacy-enhancing techniques, useful when the generation method and re-identification testing are handled with care, and weak when they are not.
Used Well
Handled properly, synthetic data lets teams move faster on AI development while keeping real records where they belong. The governance work does not disappear, it shifts to proving that the synthetic set cannot be unpicked back to the people it was modelled on.
Source: ICO guidance on anonymisation, pseudonymisation and privacy-enhancing technologies (March 2025).
Govern the data behind your AI
HEX 165 assesses AI systems against the obligations that apply to them, including how training and test data are handled. Book a demo or learn more about the platform.