The AI Index Report 2025 points to synthetic data becoming a more common input to AI training as privacy constraints tighten and generation methods improve. The tradeoff: teams now need defensible quality validation plus audit-ready privacy and compliance controls.
AI Index Report 2025: synthetic data goes mainstream, and governance has to catch up
The AI Index Report 2025 highlights increasing use of synthetic data for training AI systems, attributing the shift to two forces: stricter privacy expectations and improving synthetic data generation technology. In the report’s framing, synthetic data is moving from a niche workaround to a practical option for organizations trying to unlock model development while reducing exposure tied to sensitive or regulated personal data.
At the same time, the report flags compliance and ethical challenges that come with broader adoption. For operators, that means synthetic data can’t be treated as a blanket “privacy solved” label. Data teams are expected to show that synthetic datasets are fit for purpose (utility) while also demonstrating privacy protection and regulatory compliance (risk). The report’s implication is clear: as synthetic data becomes more embedded in pipelines, the burden shifts from experimentation to governance—testing, documentation, and controls that hold up under review.
- Quality proof becomes a deliverable, not a nice-to-have. Teams adopting synthetic data for training need repeatable validation that synthetic distributions and edge cases support downstream model performance—not just “looks realistic.”
- Privacy claims need evidence. Privacy engineers should expect to operationalize risk testing and governance (e.g., documented evaluation, approvals, and audit trails) to substantiate that synthetic data meaningfully reduces exposure versus the original data.
- Compliance work shifts left into the data pipeline. As rules evolve, founders and compliance leads will need clearer decision records on when synthetic data is acceptable, how it was generated, and what controls prevent re-identification or misuse.