Synthetic data is increasingly treated as the default “safe data” layer for analytics and AI, not a niche technique. A Gartner Peer Community survey cited by K2View pegs synthetic generation as the primary anonymization method for most organizations, with adoption extending beyond tabular data into text and images.
K2View cites Gartner survey: synthetic data leads anonymization adoption
K2View highlighted results from a Gartner Peer Community survey indicating that 84% of organizations use synthetic data generation as their primary method for anonymization. The survey also breaks out usage by modality: synthetic text is used by 84% of organizations, while synthetic image data is used by 54% and synthetic tabular data by 53%.
In practical terms, the numbers suggest synthetic data has moved from “pilot” to “program” for many teams, with common use cases spanning software testing, analytics, and model training. The write-up also notes a growing pattern of pairing synthetic data with other privacy-preserving approaches—specifically federated learning and differential privacy—to support work in regulated environments.
- Benchmarking for data leaders: The 84% figure is a useful reference point for assessing whether your anonymization roadmap is lagging, and whether synthetic data is positioned as a core control or an ad hoc workaround.
- Modality coverage is now table stakes: High reported use of synthetic text and meaningful adoption for images implies teams should plan for governance and quality checks beyond tabular (e.g., prompt/label leakage, memorization risk, and provenance tracking).
- “Synthetic” doesn’t replace privacy engineering: The mention of federated learning and differential privacy is a reminder that many regulated use cases will demand layered controls—utility metrics, privacy risk testing, and clear policy on where synthetic data is acceptable (and where it isn’t).
- Procurement and compliance implications: If synthetic data is the primary anonymization method, security reviews should treat generators as sensitive infrastructure (access controls, audit logs, reproducibility, and evidence packages for regulators and internal audit).