Current Volume 8
Abstract
Multi-cloud enterprise adoption of AI solutions now faces fresh obstacles to safeguard data sovereignty since data should operate under national laws of its collection locale. Multinational companies that depend on distributed cloud methodologies for scalability and efficiency seek to perform AI-powered analytics while managing diverse regulations that control where and how they handle data and transfer it between borders. Organizations now need to follow three categories of strict data compliance policies: GDPR, CCPA, and EU AI Act that make them implement secure data handling systems to match local data protection standards. The enforcement of data sovereignty becomes complex in multi-cloud systems because of substantial challenges and security risks they produce. Data distribution across multiple storage systems together with system compatibility problems and security holes and inconsistent regulatory standards result in the risk of data breaches and regulatory noncompliance and information control loss for business-critical data. Sovereignty requirements throughout multiple cloud providers become essential with AI-powered applications that process large volumes of sensitive data because data protection measures and encryption and access control need to be established at once. When enterprises lack proper governance systems they face problems that include regulatory penalties as well as regulatory conflicts with data residency requirements and impaired AI decision systems. A detailed examination exists within this paper about how AI-powered multi-cloud enterprises should tackle legal, technical and operational challenges to guarantee data sovereignty. The paper delivers thorough examinations of data compliance standards while defining proper AI governance methods together with secure procedures for protecting data distributed across multiple cloud platforms. The research investigates data localization approaches as well as secure AI processing practices and encryption-based sovereignty methods which businesses can employ to decrease their vulnerabilities. The research uses financial sector and healthcare sector and public sector case studies to present effective methods which lead to AI performance enhancement alongside regulatory adherence. Additionally this document reviews current sovereign cloud trends together with AI-based regulatory enforcement methods alongside automated compliance tracking methods which assist businesses in managing AI security together with cloud governance developments. Researchers offer specific solutions which businesses need to reach their maximum AI potential while sustaining regulatory compliance along with high security standards and upholding ethical responsibilities.
Keywords
Data Sovereignty, Multi-Cloud Security, AI Governance, Regulatory Compliance, Data Localization
Citations
IRE Journals:
Shishir Tewari , Ashitosh Chitnis
"AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty" Iconic Research And Engineering Journals Volume 7 Issue 7 2024 Page 571-587
IEEE:
Shishir Tewari , Ashitosh Chitnis
"AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty" Iconic Research And Engineering Journals, 7(7)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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