AI Governance Engineer
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Synthetic data

By Updated

Data generated by a model or simulation rather than collected from people or events, used to augment training sets, test edge cases or reduce exposure of personal data. It inherits the biases of its generator and can leak the records it was fitted on, so it is tested like any other dataset.

Developed in
ch. 14, Synthetic data, augmentation and privacy-enhancing technologies
Chapters
ch. 14, Development · ch. 19, Privacy & AI
Source
Defined by this Body of Knowledge: the section it is developed in is the source

Where it is used

5 chapters of the Body of Knowledge use the term. Each link opens the first section that does.

Patterns that use this term

One pattern page uses the term.

  • Privacy-enhancing technology (PET)

    A technique that reduces what an attacker, vendor or insider can learn from personal data, such as differential privacy, federated learning, synthetic data…

Sources

No external source: the term is coined or used in a specific sense by this Body of Knowledge, and the section linked under "Developed in" is its source.

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Synthetic data. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/synthetic-data. CC BY 4.0

BibTeX

@misc{aige2026syntheticdata,
  author  = {Jorge García Aibar},
  title   = {{Synthetic data}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/synthetic-data}
}