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.

AI governance refers to the set of legal, institutional, and organizational mechanisms used to shape the development, deployment, and use of artificial intelligence systems. These mechanisms include formal regulation, private law rules, standards-setting, market design, and internal governance practices within firms and public institutions.

In legal scholarship, AI governance is often contrasted with purely technical approaches to safety or alignment. Rather than focusing on how systems behave internally, governance frameworks emphasize how incentives, constraints, and accountability structures influence behavior externally.

AI governance is not limited to public regulation. It also encompasses private ordering through contracts, liability regimes, industry standards, and market institutions that condition access to data, compute, and deployment environments.