Data governance refers to the legal, institutional, and organizational frameworks that determine how data is collected, accessed, shared, and used. In the context of artificial intelligence, data governance shapes which datasets are available for training, validation, deployment, and oversight, and under what conditions.
Legal approaches to data governance include privacy and data protection law, intellectual property and trade secret regimes, contractual restrictions, and sector-specific access rules. Institutional and market-based mechanisms, such as standards, licensing practices, and platform rules, also play a significant role in structuring data access.
In AI law and policy, data governance is often treated as a primary lever for influencing system behavior indirectly, by conditioning the inputs on which models depend rather than regulating model outputs directly.