Synthetic data is increasingly positioned as a practical way to train AI systems while reducing privacy exposure and supporting compliance. The catch: many teams still lack the skills and controls to generate synthetic datasets that are representative enough to avoid degrading model performance.
Synthetic data moves from “nice-to-have” to privacy workflow staple
In its brief AI Privacy in 2025: Trends, Challenges, and Solutions, Synthetic Data News argues that organizations are expected to expand synthetic data use to reduce the privacy risk of using real personal or sensitive data in AI training. The thesis is straightforward: synthetic data can help teams keep development moving while lowering exposure to data breaches and aligning day-to-day pipelines with regulatory expectations such as GDPR and CCPA.
The piece also flags two adoption constraints that show up repeatedly in real implementations: a skills gap (teams don’t know how to generate and validate synthetic datasets) and persistent concerns about synthetic data quality and representativeness versus real-world distributions—issues that can directly impact downstream model performance.
- Privacy engineering: Synthetic data can reduce the blast radius of breaches and limit access to raw personal data, but it still requires governance (who can generate, what can be exported, and how it’s audited).
- ML performance risk: “Synthetic” isn’t automatically “useful.” If fidelity and edge cases aren’t preserved, teams can ship models that look good in offline tests but fail in production.
- Compliance operations: Synthetic datasets can support GDPR/CCPA-aligned workflows by minimizing use of real data, but organizations still need documented processes to justify when and how synthetic data is used.
- Capability build vs. tool purchase: The biggest bottleneck is often expertise—data teams may need new tooling plus training to generate representative synthetic datasets without introducing bias or losing signal.