How do I handle data residency and sovereignty requirements for AI workloads?

To handle data residency and sovereignty requirements for AI workloads, implement data classification, routing logic, and comprehensive data governance across the AI system lifecycle. This ensures data is processed and stored in compliant regions and that its classification is maintained throughout its use.

• Implement Data Classification and Propagation: Assign residency labels to all data at ingestion, and ensure these classifications propagate to all derived data, such as embeddings, summaries, and logs. This is a key aspect of Data & Memory Governance.
• Enforce Routing Logic: Develop routing logic at the inference layer that respects data residency labels, preventing data from being processed in non-compliant regions. This addresses the OWASP LLM Top 10 risk L4 (Data Residency Violations).
• Maintain a Data Inventory: Create and continuously update a data inventory that maps where personal and sensitive data resides, how it flows, who has access, and its retention period. This artifact is crucial for compliance and incident response.
• Partition Memory Stores: Partition memory stores, including vector databases, by tenant and classification level to prevent data leakage and ensure strict per-tenant memory scoping. This mitigates OWASP LLM Top 10 risk L2 (Memory contamination across sessions or tenants).
• Govern Data Throughout its Lifecycle: Establish robust data governance practices covering provenance, quality, preparation, and management of data used by AI systems across its entire lifecycle. This aligns with ISO/IEC 42001 A.7 (Data for AI systems).
• Address Right-to-Erasure: Implement per-user data inventory across all stores and deletion workflows that propagate to derived data to ensure compliance with right-to-erasure requests. This addresses OWASP LLM Top 10 risk L2 and L6 (Right-to-erasure failures).