Data Sovereignty in Global AI-Blockchain Infrastructure

Abstract

As artificial intelligence (AI) systems scale across borders and decentralized ledgers interconnect global networks, a central challenge emerges: how to ensure data sovereignty—the ability of jurisdictions, organizations, and individuals to exert legitimate control over data—without stifling innovation or undermining the integrity and utility of distributed architectures. This manuscript proposes a comprehensive, practice-oriented blueprint for embedding data sovereignty into AI-blockchain infrastructure. We first synthesize the legal and policy landscape shaping cross-border processing (e.g., GDPR, Schrems II, SCCs, EU–U.S. Data Privacy Framework, EU AI Act, Data Governance Act, Data Act, CLOUD Act, India’s DPDP Act, China’s PIPL, OECD/G7 initiatives, and Global CBPR). We then examine technical levers—on-chain/off-chain partitioning, permissioned topologies, sovereign cloud patterns, privacy-enhancing computation, verifiable provenance, and risk management frameworks—to operationalize jurisdictional constraints without losing decentralization benefits. Building on this review, we introduce SOVEREIGN-Stack, a governance-by-design methodology spanning eight layers (identity, consent, data classification, locality & routing, compute & model governance, ledger governance, transfer mechanisms, and assurance/tooling). Two applied vignettes—in health analytics spanning the EU, U.S., and India, and a permissioned supply-chain ledger touching the EU and APAC—demonstrate how the approach balances legal obligations (erasure, purpose limitation, transfer restrictions) with architectural needs (immutability, integrity, transparency). We conclude with a set of implementation checkpoints and maturity indicators that organizations can use to align AI-blockchain roadmaps with evolving global rules while maintaining verifiability, auditability, and performance.