The OECD is framing synthetic data as part of the broader policy stack for AI, data governance, and privacy. The report focuses on how governments and organizations can support research use while reducing exposure of personal data.
AI, Data Governance and Privacy: Synergies and Areas of International Co-operation
The OECD report examines how AI policy, data governance, and privacy rules increasingly overlap, with synthetic data presented as one mechanism that can help reconcile access to data with protection of personal information. Rather than treating synthetic data as a standalone technical fix, the report places it inside a wider governance framework that includes accountability, lawful access, safeguards, and cross-border co-operation. That framing matters because many AI development and research workflows now depend on moving data across teams, institutions, and jurisdictions.
The report’s core premise is practical: organizations need ways to enable analysis, testing, and model development without defaulting to unrestricted use of sensitive datasets. In that context, synthetic data is highlighted as a privacy-supporting approach that may expand access for research and innovation while still requiring careful governance around quality, residual risk, and intended use. The OECD also emphasizes international co-operation, reflecting the reality that AI systems, vendors, and data supply chains rarely stop at national borders.
- Synthetic data is being treated as a governance tool, not just a technical substitute, which means adoption decisions will increasingly sit with privacy, legal, and policy teams as well as ML engineers.
- Data teams should expect pressure to document when synthetic data is appropriate, what privacy protections it provides, and where it does not remove the need for access controls or oversight.
- For organizations operating across jurisdictions, the report is a reminder that cross-border AI work will run into uneven expectations on privacy, data sharing, and accountability, even when synthetic data is used.
- Research leaders and product teams can read this as policy support for privacy-preserving data access models, but not as a blanket endorsement that synthetic data automatically solves compliance or utility problems.