AI regulations increasingly mandate specific data storage and retention practices. The EU AI Act (paired with GDPR) requires high-risk system developers to maintain training data records for the lifetime of the system plus several years after, enabling regulators to audit how the model was built. Limited-risk systems (chatbots) must log user interactions for a minimum period (typically 30-90 days under various state laws) to investigate complaints. Data must be stored securely with access controls restricting who can view it.
Data minimization is a core requirement: store only data necessary for the AI system to function. This conflicts with the typical ML development cycle, which loves hoarding data for retraining and analysis. Regulations force a choice: (1) minimal approach—store only inference inputs/outputs needed for audit, or (2) explicit consent—ask users if you can store their data for model improvement. Washington’s HB 1170 doesn’t explicitly mandate data minimization, but the content provenance requirement implicitly does: if you must track which documents were used to generate outputs, you can’t blindly store all user interactions.
For teams using Milvus, data storage rules reshape your architecture. Separate training embeddings (kept long-term for compliance) from inference embeddings (refreshed regularly). Create immutable collections for audit data—store decision logs with timestamps, model versions, and input data separately from operational vectors. Implement collection-level access control to restrict who can query sensitive embeddings. For open-source Milvus, configure your backend storage (Minio, S3) with versioning and access logging to prove you’re meeting data governance requirements. For Zilliz Cloud, leverage built-in data residency controls to ensure embeddings stored in EU regions comply with EU data localization rules, and use automated backup policies to demonstrate compliance with minimum retention periods.