Global Synthetic Data
Usage & Regulation Guide

As artificial intelligence systems demand increasingly massive datasets, the traditional methods of data acquisition have hit a wall of privacy, ethical, and logistical constraints. Synthetic data—artificially generated information that mimics the statistical properties of real-world data without containing private information—has emerged as a critical Privacy-Enhancing Technology (PET). Global regulators are now transitioning from general AI principles to specific frameworks that recognize synthetic data as a viable path for innovation [EU1][KR1].

The EU AI Act, finalized in 2024, sets the world's most comprehensive risk-based framework for AI. While it encourages synthetic data for debiasing high-risk models, it mandates strict transparency for generative outputs. Furthermore, the GDPR continues to apply to synthetic data if the 're-identification risk' is not sufficiently mitigated. The European Data Protection Supervisor (EDPS) emphasizes that synthetic data is not a 'get out of jail free' card for privacy, but a powerful tool when combined with rigorous technical validation [EU1][EDPS1].

The U.S. approach is defined by Executive Order 14110 and the NIST AI Risk Management Framework. The focus here is on safety and 'content provenance.' NIST is developing standards for watermarking and detecting synthetic content to prevent data poisoning and misinformation. Synthetic data is viewed as a strategic asset for federal research and a method to ensure data minimization in high-stakes sectors like healthcare and finance [US1][NIST1].

Japan follows a 'soft law' approach, led by METI's AI Governance Guidelines. By focusing on non-binding benchmarks and industry checklists (updated in 2025), Japan aims to foster an innovation-first environment. Their framework specifically addresses the contractual side of data sharing, providing templates for how synthetic data should be treated in intellectual property and liability agreements [JP1].

South Korea's PIPC is among the most proactive regulators in Asia. In 2024, they released specific reference models for synthetic data generation across sectors like healthcare and finance. By 2025, they introduced a 4-stage lifecycle guide for Generative AI, requiring safety measures from data collection to deployment. The Korean framework explicitly recognizes synthetic data as a key solution for training large-scale models while maintaining compliance with the Personal Information Protection Act (PIPA) [KR1][KR2].