AI Glossary

Synthetic Data

Synthetic data is artificially generated data used for training, testing, or privacy-preserving sharing when real data is scarce or sensitive.

Definition

Synthetic data is artificially generated data used for training, testing, or privacy-preserving sharing when real data is scarce or sensitive.

Plain English explanation

Made-up-but-realistic data created by software to train or test systems.

Technical explanation

Synthetic data is generated by models or simulators for training, testing, or privacy-preserving sharing. Utility and leakage risks must be evaluated—not assumed.

Why it matters

Teams use synthetic data when real data is scarce, sensitive, or imbalanced—but poor synthetics can mislead models.

Real-world applications

  • Augmenting rare classes
  • Privacy-preserving sharing
  • Simulation for robotics/autonomy

Benefits

  • Can fill data gaps
  • May reduce exposure of raw PII when done carefully

Limitations

  • Distribution mismatch
  • Potential privacy leakage from generative models

Common misconceptions

  • Synthetic does not automatically mean anonymized
Privacy Generative AI

Related industries

Marketing

Related companies

Related products

FAQ

Is synthetic data always safe to share?

No. Evaluate re-identification and membership risks; use privacy techniques when required.

Last reviewed

Sources

  • Synthetic data privacy and utility literature

Correction request

If a technology assignment or hub description is inaccurate, submit a correction via the Corrections Policy.

Knowledge Library → · All technologies → · AI Models → · APIs & SDKs → · Integrations → · Compliance → · Browse all companies → · Explore industries → · Compare →