Zero-Trust Security Architectures and Confidential Computing for Multi-Tenant Cloud AI

Multi-tenant cloud artificial intelligence platforms process proprietary model weights, regulated customer prompts, and high-value fine-tuning datasets across shared physical hosts. Traditional perimeter-based cloud security assumes that traffic inside a virtual private cloud (VPC) is trustworthy and that the cloud provider's hypervisor, host operating system kernel, and cluster administrator cannot inspect guest memory. Both assumptions fail under modern threat models involving container escapes, compromised management planes, supply-chain poisoning of model artifacts, and side-channel memory inspection. This paper presents a defense-in-depth Zero-Trust and Confidential Cloud AI architecture that eliminates implicit trust across network, identity, and hardware memory layers. Aligned with NIST Special Publication 800-207 (Rose et al., 2020) and Google BeyondCorp principles (Ward & Beyer, 2014), the proposed architecture combines SPIFFE/SPIRE short-lived X.509 workload identities, eBPF-enforced mutual TLS (mTLS) micro-segmentation, and hardware-rooted Trusted Execution Environments (TEEs) spanning AMD SEV-SNP, Intel TDX host enclaves, and NVIDIA H100 Confidential Computing modes (Dhanuskodi et al., 2023; Kaplan et al., 2020). Cryptographic remote attestation binds key release from cloud key management systems (KMS) directly to verified hardware measurement registers (RTMRs), ensuring that encrypted model weights and tenant datasets are decrypted exclusively inside attested enclave memory. We evaluate the architecture through a production case study modeled on Google BeyondCorp Enterprise and Confidential Google Kubernetes Engine (GKE) / Azure Confidential AI deployments serving healthcare and financial workloads. Empirical benchmarks show that enabling full CPU-GPU confidential enclaves with bounce-buffer DMA encryption imposes only a 4.8% to 7.9% throughput overhead on 70B LLM inference while reducing lateral movement blast radius to zero and satisfying HIPAA, PCI-DSS 4.0, and EU sovereign cloud mandates. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Cloud Security, Hardware Enclaves, and Zero-Trust Infrastructure. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.