AI Data Residency refers to the geographic and jurisdictional requirements governing where data can be stored, processed, and transferred when used with AI systems. As organizations adopt cloud-based AI services, ensuring data residency compliance has become a critical governance concern.
Data residency challenges specific to AI include: AI service hosting locations (many AI providers process data in the US, even for international customers), training data geography (where the data used to train models was sourced and stored), inference processing location (where user prompts and AI responses are processed), data caching and logging (intermediate storage of AI interactions), model training on user data (whether user inputs are used to train models and where that training occurs), and cross-border data transfers triggered by AI API calls.
Regulatory frameworks imposing data residency requirements include: GDPR (EU/EEA data transfer restrictions), China's PIPL and Data Security Law, Russia's data localization law, India's Digital Personal Data Protection Act, Brazil's LGPD, and various industry-specific regulations in financial services and healthcare.
Organizations should evaluate AI vendors for data residency capabilities, implement technical controls to prevent data from crossing jurisdictional boundaries, maintain records of where AI processing occurs, and regularly audit AI data flows for residency compliance.
