Secure Execution of AI Pipelines on Confidential Cloud Infrastructure

Abstract

With the explosive growth of artificial intelligence (AI) services in recent years, organizations are increasingly relying on cloud platforms to execute end-to-end AI pipelines—spanning data ingestion, preprocessing, model training, and inference. While cloud infrastructures offer unparalleled scalability and cost advantages, they also introduce significant risks: untrusted hypervisors, co-tenant attacks, and privileged insider threats can expose sensitive data and proprietary model parameters. Confidential computing, realized via hardware-enforced Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV, seeks to mitigate these risks by isolating code and data within protected enclaves. Despite the promise of TEEs, integrating them seamlessly into existing AI toolchains presents architectural, performance, and usability challenges. This manuscript presents SecureAI, a comprehensive framework for orchestrating AI workflows on confidential cloud infrastructure. We detail enclave provisioning, secure data ingestion, framework adaptation for TensorFlow and PyTorch, distributed parameter management, and end-to-end attestation. Through rigorous security analysis, we enumerate threat models and countermeasures. Empirical benchmarks on CIFAR-10 training with ResNet-50 quantify overheads: SGX enclaves incur ~25% runtime overhead, while AMD SEV adds ~17%. A Kubernetes-based simulation of mixed SGX/standard nodes highlights scheduling strategies that balance security and throughput. Our results demonstrate that SecureAI achieves strong confidentiality and integrity guarantees with acceptable performance trade-offs, paving the way for practical deployment of secure AI services in the public cloud.