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Zero-Trust Architectures for Secure Multi-Cloud AI Workloads

Author(s) Srikanth Jonnakuti
Country United States
Abstract The rapid pace of AI adoption, with the purchase of distributed machine learning pipelines in heterogeneous cloud environments now being the highest priority. This article introduces an AI pipeline architecture design tailored for federated model training environments spanning across AWS, Azure, and GCP. The architectured vision is centered on zero trust principles by per-service authentication to ensure only authenticated services can access or contribute to model training jobs. To protect data in transit, end-to-end encryption methods are infused via TLS 1.3 and platform-native secure transport layers. Real-time anomaly detection and ongoing monitoring and logging are done through the pipeline using centralized telemetry collectors and SIEM tools. The architecture is designed to be ready to handle model parameter exchanges securely, enable data privacy compliance (GDPR, HIPAA), and meet audit-ready environments. Federated learning nodes utilize containerized workloads orchestrated by Kubernetes clusters operated by all cloud providers. A policy enforcement layer verifies the metadata of each training session, access control context, and cryptographic integrity before execution. For observability, monitoring agents stream metrics and logs into a shared dashboard with cross-cloud aggregation functionality. Identity federation is also managed by open standards like OIDC and SAML, which facilitate service-to-service authentication without any glitches. This architecture enhances resilience, transparency, and operational trust in federated AI processes. Comparative evaluation shows its capability to reduce latency and breach exposure. The architecture is also scalable and cloud resource elasticity-friendly, making it ready for multi-tenant deployment in healthcare, finance, and defense applications.
Field Engineering
Published In Volume 2, Issue 5, May 2021
Published On 2021-05-08
Cite This Zero-Trust Architectures for Secure Multi-Cloud AI Workloads - Srikanth Jonnakuti - IJLRP Volume 2, Issue 5, May 2021. DOI 10.70528/IJLRP.v2.i5.1558
DOI https://doi.org/10.70528/IJLRP.v2.i5.1558
Short DOI https://doi.org/g9nhp9
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