Data sovereignty refers to the principle that data is subject to the laws of the country in which it is collected or stored. For AI systems that depend on large, diverse datasets and often run on cloud infrastructure spanning multiple jurisdictions, this creates complex compliance challenges. The EU's GDPR restricts transfers of personal data outside the European Economic Area unless the receiving country provides adequate data protection. China's data localisation laws require certain types of data to remain within Chinese borders. India, Brazil, and numerous other countries have their own data sovereignty requirements. For organisations building or deploying AI internationally, these regulations affect everything from where you can train your models to which cloud providers you can use to how you handle customer data. A model trained on data from multiple countries may need to comply with the data protection laws of all of them. Cloud-based AI services that route data through servers in different jurisdictions add further complexity. The practical response involves understanding exactly where your data is stored and processed, ensuring your cloud and AI provider agreements include appropriate data handling provisions, and designing your architecture to accommodate jurisdictional requirements. This is an area where early legal and compliance engagement saves significant pain later.