Two publications examine synthetic data from different sides of the same problem: whether an organization can substantiate what it has generated and how it can use it. For data teams, validation is not just a model-development task; its records may also matter in an acquisition.
Synthetic Data Risk Guide: Privacy, Quality, and Model Collapse
NHI Management Group’s guide examines privacy, quality, contamination, and security risks in synthetic-data pipelines. Its central caution is that generated records should not be treated as safe or useful simply because they are synthetic. A dataset can fail on more than one dimension: it may be unsuitable for a training task even if it passes a privacy check, or introduce risks that are missed when teams assess utility alone.
That makes validation a continuing governance question rather than a one-time label applied at generation. Teams need to know which source data and generation process produced a dataset, what checks were performed, and whether those checks remain relevant when the dataset is reused. Without that context, it is difficult to investigate a model-quality problem or explain why a synthetic dataset was approved for a particular workflow.
- Test privacy risk and task-specific quality separately, so a favorable result on one measure does not obscure a failure on the other.
- Check for contamination before training or evaluation, because compromised inputs can make model results unreliable even when the records appear plausible.
- Record dataset versions, validation results, and approval decisions so teams can revisit the evidence when a use case changes or a problem emerges.
Synthetic Data as a Deal Asset: Ownership, Provenance, and Diligence Considerations in AI Acquisitions
Mayer Brown considers synthetic data as an asset in AI acquisitions, focusing on uncertainty around ownership, provenance, quality, and regulatory treatment. The publication shifts the diligence question from whether a target possesses a dataset to whether it can explain how that dataset was made and what rights or restrictions may attach to it. Model weights and code alone do not answer those questions.
For buyers, technical documentation and legal review therefore have to meet at the data pipeline. A provenance record can help identify source inputs, generation methods, and downstream uses for review; it does not, by itself, settle ownership or compliance. Where that record is incomplete, the parties may have less confidence in how the asset can be represented, valued, or used after closing.
- Ask for a traceable account of source data, generation, and permitted uses before treating a synthetic dataset as a transferable deal asset.
- Bring data and legal teams into the same diligence review, because technical quality evidence cannot resolve ownership questions on its own.
- Assess regulatory treatment against the dataset’s creation and intended use rather than assuming the word “synthetic” removes compliance obligations.