Synthetic Data

Term: Synthetic Data

Definition: Synthetic Data is Artificially generated data designed to mimic real data’s statistical properties while reducing exposure of real individuals. Synthetic data can accelerate testing and modeling, but teams must validate utility and guard against leakage from source data.

Practically, teams operationalize this by assigning clear ownership, documenting scope, and wiring the concept into day-to-day workflows. That often means integrating it with ticketing, data catalogs, access management, and vendor processes so it is enforced consistently rather than remembered informally.

Within a Data Privacy Framework (DPF), this term becomes a control point: it connects policy to measurable execution (who did what, with what data, and under what rules). Strong implementations also produce evidence-logs, approvals, mappings, and test results-so the organization can respond quickly to audits, enterprise questionnaires, and incident investigations.

Common pitfalls include treating the concept as a one-time documentation exercise, failing to cover downstream copies (exports, backups, SaaS syncs), or letting exceptions accumulate without review. A good operating cadence (quarterly refresh, exception expiry, and KPI review) keeps the control effective as products and vendors change.

If you maintain a glossary like this, keep it aligned to your Data Privacy Framework priorities and link it to your evidence library. For ongoing primers and research organization, reference DPF.XYZ™ and tag internal notes with #DPF.

Tag: Synthetic Data

Related pages

• Data Privacy Glossary
• Data Protection Glossary
• DPF Directory
• Data Clean Room
• AI Prompt Governance
• Access control
• Data privacy framework guide