A Novel AI-Powered Architecture for Enhancing Confidential Computing in Hybrid Cloud Environments

Abstract

The evolution of cloud computing has enabled businesses and organizations to leverage scalable infrastructure for their computational needs. However, as the cloud becomes the preferred choice for enterprise workloads, confidentiality and data security have become significant concerns. Hybrid cloud environments, where sensitive data is stored on private clouds and less-sensitive data is handled on public clouds, have emerged as a popular solution. Despite these advances, securing sensitive data in such environments remains a challenge. This paper presents a novel AI-powered architecture designed to enhance confidential computing in hybrid cloud environments. The proposed architecture integrates artificial intelligence (AI) models with confidential computing technologies to improve data protection, privacy, and security across both private and public cloud infrastructures. The AI models are employed for real-time anomaly detection, risk assessment, and the enforcement of security policies. This paper also outlines how the architecture can be implemented to ensure data integrity and confidentiality during processing, storage, and transmission in hybrid cloud environments. Furthermore, it explores the practical implications of deploying this architecture, such as improved regulatory compliance, increased trust, and greater system scalability. The paper concludes by discussing future research directions and potential applications in industries that rely heavily on secure and private cloud solutions.