Two governance signals matter today: California is moving toward mandatory disclosure of generative AI training data information, while the OECD is outlining where AI, privacy, and data governance frameworks align—and where they still conflict. For teams using synthetic data, the direction is clear: documentation and defensible controls are becoming baseline requirements, not optional process work.
California Assembly Bill 2013 would force training-data disclosure into the open
California Assembly Bill 2013 (2024) requires developers of generative AI systems to publicly disclose information about the data used to train their models, with the requirement taking effect on January 1, 2026. The measure is aimed at increasing transparency around how generative systems are built and what data sources sit behind them.
For synthetic data practitioners, the bill matters less as a narrow state rule than as a compliance template: if training inputs or data-generation methods need to be described publicly, teams will need stronger lineage records, clearer provenance documentation, and a more explicit account of where synthetic data was used in model development.
- Disclosure obligations raise the bar for data provenance, including how synthetic data is created, labeled, and mixed with real-world data.
- Model developers may need to build documentation workflows now, rather than waiting for the January 1, 2026 effective date.
- Public transparency requirements can create downstream pressure from enterprise buyers, auditors, and regulators beyond California.
OECD maps the fault lines between AI, data governance, and privacy
The OECD’s report, AI, Data Governance and Privacy: Synergies and Areas of Tension, examines how AI systems intersect with privacy and broader data governance frameworks. Its core message is that there are real synergies between these domains, but also persistent tensions that require more robust governance structures—especially when organizations use techniques such as synthetic data to manage privacy risk while still enabling model development.
That makes the report directly relevant to teams positioning synthetic data as a privacy-preserving solution. The OECD does not treat synthetic data as a simple compliance shortcut; instead, it places it inside a wider governance problem that includes accountability, risk management, and the need for frameworks that can support responsible AI deployment at scale.
- Synthetic data may reduce some privacy exposure, but governance expectations still apply around accountability, controls, and intended use.
- Privacy, AI governance, and data management cannot be handled as separate workstreams if synthetic data is part of production pipelines.
- The report gives policy and compliance teams a reference point for evaluating whether current synthetic data practices are mature enough for enterprise use.