What is synthetic data?
Synthetic data is artificially generated data that preserves the statistical properties of a real dataset without reproducing its records: produced by generative models, simulations or rule-based engines, and used for testing, sharing and model training where the real data is sensitive, scarce or legally encumbered.
The two legal moments
Teams habitually analyse only the output. There are two processing moments: generating the synthetic set requires processing the source personal data (legal basis, purpose-compatibility, stricter rules for special categories), and using the output escapes data protection law only if the output is genuinely anonymous. “Synthetic” is an engineering label, not a legal status: generative models memorise outliers, and rare combinations can leak from source to output. The test is the same as for anonymization: singling out, linkability, inference: evidenced by membership-inference and similarity testing, not by vendor claims.
Where it earns its keep
Three sweet spots for a startup: production-like test data without copying customer records into staging; sharing data-shaped material with partners, investors or offshore teams without triggering transfer machinery; and training-data augmentation for scarce classes. The AI Act expressly recognises synthetic and anonymised data within the data-governance duties for high-risk systems, so a documented generation pipeline is a compliance asset, not just an engineering convenience.
Is synthetic data anonymous by definition?
No. It is anonymous only if it passes the same re-identification analysis as any anonymization claim: with rare records and small cohorts as the usual failure points.
Can synthetic data “launder” unlawfully collected data?
No: the generation step processes the source data, so unlawful collection contaminates the pipeline. Fix the basis first, then generate.
Related: pseudonymization, DPIA.
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