Open Access

Confidential AI Cloud Architecture for Secure Enterprise Data Processing and Intelligent Workloads

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Jeyakumar Ramachandran ¹ ,

⁴ Independent Researcher, USA

Abstract

The increasing adoption of generative and agentic artificial intelligence in enterprise environments has introduced new requirements for protecting sensitive data during AI model training, inference, and automated decision-making. Traditional encryption mechanisms primarily protect data at rest and in transit but provide limited protection while data is actively processed. This paper proposes a confidential AI cloud architecture that combines confidential computing, trusted execution environments, secure workload orchestration, identity-based access control, and Kubernetes-based infrastructure to protect sensitive enterprise AI workloads. The architecture provides hardware-backed isolation and attestation mechanisms for sensitive data and AI processing while supporting scalable cloud-native deployment models. The proposed framework addresses security, privacy, compliance, workload isolation, and operational governance requirements for enterprise AI platforms. Confidential computing provides a foundation for protecting sensitive AI workloads and data in use.

Keywords

Confidential Computing, Generative AI, Kubernetes, Enterprise Cloud

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