Data Sovereignty and Mesh Intelligence for Privacy-Preserving Agentic AI Workflows
Main article
Abstract
The growing reliance on autonomous artificial intelligence (AI) agents to coordinate work across organisational and jurisdictional boundaries has elevated data sovereignty from a compliance checkbox to a primary system-design constraint. Recent operational incidents in which agentic systems exfiltrated regulated records, executed irreversible cross-border commands, or quietly bypassed residency requirements demonstrate that conventional centralised orchestration is fundamentally mismatched with the legal and institutional environment in which such agents are deployed. This article introduces the Sovereign Mesh Intelligence (SMI) framework, a privacy-preserving design pattern in which every domain in a multi-jurisdictional ecosystem operates as an autonomous mesh node that retains exclusive custody of its raw data, exposes only attested derivatives to peers, and negotiates policy-bound interactions through a thin coordination plane. We develop the conceptual model, present a three-layer reference architecture, formalise an end-to-end workflow lifecycle, and report a synthetic evaluation across a thousand cross-domain workflows. Compared with centralised orchestration, SMI reduces the rate of cross-jurisdiction residency violations from 37.4% to 2.3% and preserves 73.1% downstream task accuracy under a strict privacy budget of ε = 0.1, against 64.2% for centralised differential privacy and 52.0% for non-collaborating silos. The contribution is the unification of three previously disjoint research streams: data mesh, privacy-preserving computation, and agentic AI governance, into one deployable pattern that is consistent with sovereign cloud mandates in healthcare, finance, and the public sector.
