Compare synthetic data approaches
Different data privacy techniques make different tradeoffs. Understanding those tradeoffs — particularly around whether real data is processed, where the compliance exposure sits, and what documentation exists — is essential for AI governance.
GDPR · HIPAA · PCI DSS
Synthetic data vs data masking
Data masking transforms real records — the source data is still processed and stored. Synthetic data generates new records with no derivation from individual real records.
Read comparison →GDPR · EU AI Act · HIPAA
Synthetic data vs federated learning
Federated learning trains models without centralizing data — but models still learn from real data at source. Synthetic data removes real data from the training pipeline entirely.
Read comparison →Why the choice of technique matters for AI governance
AI systems built on sensitive data face a core question: at what point in the pipeline was real personal or proprietary data processed? Regulators — whether under GDPR, HIPAA, the EU AI Act, or sector-specific frameworks — are increasingly asking AI developers to document that question.
Techniques like data masking, tokenization, and k-anonymization modify real records but do not eliminate the compliance obligation. The source data was still accessed, processed, and transformed. Re-identification risks persist — and the legal processing activity still occurred.
Federated learning avoids centralizing data, but the model weights encode information learned from real data at source. The training process touches real records, even if they never leave the originating institution.
Synthetic data generated by statistical models — and certified with a tamper-evident artifact — creates a documented break: no real records were used in this training dataset. The CertifiedData certificate provides machine-verifiable evidence of that break, supporting compliance documentation, IRB research protocols, and AI system audits.
Generate certified synthetic data for your use case
CertifiedData generates synthetic datasets and certifies them with Ed25519-signed artifacts — creating verifiable proof that your AI training data is synthetic.