What is Anonymisation Assumptions and Documentation?

In practice, this documentation should explain the methods used, the threat model considered, the likelihood of re-identification, the datasets or auxiliary information assumed to be unavailable, and any testing performed to validate the claim. For compliance teams, the document becomes the defensible record showing why a dataset was treated as anonymised, which is critical for privacy scoping, data sharing, model training, retention, and cross-border transfer decisions.

This issue is central under GDPR and related data-protection guidance, where anonymisation is distinguished from pseudonymisation and the standard for true anonymisation is demanding. It is also important in AI governance because model training, synthetic data generation, memorization risks, and inference attacks can undermine anonymisation assumptions if they are not carefully tested and updated.