AI Governance for Multi-Cloud Data Compliance: A Comparative Analysis of India and the USA

Multinational companies that manage data across jurisdictional boundaries are having trouble integrating artificial intelligence systems with multi-cloud architectures. This is especially true since over 90% of businesses use multiple cloud providers and deal with complicated AI governance frameworks. This paper examines how well AI governance frameworks in India and the US deal with multi-cloud data residency compliance issues through a systematic literature evaluation based on PRISMA standards and thematic analysis of 26 publications, including academic articles, government policy papers, and industry reports published between 2020 and 2025. The study points out significant cross-border regulatory coordination problems and examines whether existing bilateral strategies need more ways to work together. The thematic analysis identified trends in regulatory frameworks and compliance problems, revealing "regulatory incommensurability" as a central theme—meaning that following one jurisdiction's rules goes against the basic ideas behind another's procedures. The Digital Personal Data Protection Act in India has a permission-by-default approach, which is very different from the USA's restriction-by-default approach. This leads to impossible compliance situations instead of coordination problems. Organizations face systemic inefficiencies because of duplicate infrastructure and multiple governance systems that do not provide the same level of AI safety or data security. For example, 35% of data breaches include "shadow data" not covered by existing frameworks. The results show that traditional working methods cannot settle significant disagreements between AI governance frameworks. This means that new theoretical approaches are needed that acknowledge valid regulatory differences while making it easier for multinational companies to use AI systems in both jurisdictions.