Enterprise AI Data Sovereignty and Cross-Border Governance Framework

Enterprise AI increasingly processes personal, confidential, regulated, and strategically sensitive data across global cloud regions, model providers, vector stores, data platforms, and agent tools. This creates data sovereignty challenges because data may be stored, transferred, inferred, logged, or processed across jurisdictions with different legal and contractual requirements. This paper proposes an Enterprise AI Data Sovereignty and Cross-Border Governance Framework that integrates data classification, jurisdiction mapping, residency controls, transfer mechanisms, provider due diligence, encryption, access control, logging, model restrictions, retrieval governance, and policy enforcement. The framework defines sovereignty zones, decision rules for model and region selection, cross-border risk scoring, data lineage, approval workflows, and continuous monitoring. It also addresses derived data, embeddings, prompts, model outputs, telemetry, fine-tuning data, and agent-mediated access. The proposed approach enables enterprises to scale AI globally while preserving legal defensibility, contractual obligations, business control, and data trust.