Zero Trust Architecture for Federated Generative AI: Kubernetes-Driven Personalization in Multi-Cloud Ecosystems

Abstract

In the evolving landscape of multi-cloud ecosystems, the integration of Federated Generative AI 
(FGA) with Zero Trust Architecture (ZTA) and Kubernetes orchestration offers a robust solution 
for achieving personalized decision-making while maintaining stringent security and efficient 
resource management. This study proposes a framework that leverages FGA to enhance 
personalization capabilities, utilizing Kubernetes for orchestrating AI workflows and ZTA for 
safeguarding data and system integrity. We explore the performance of the Federated Generative 
AI model across diverse datasets, demonstrating its effectiveness in delivering accurate predictions 
and handling heterogeneous data sources. The quality of synthetic data generated through 
Generative Adversarial Networks (GANs) is evaluated, showing high fidelity and minimal 
deviation from real data. System efficiency metrics highlight the benefits of Kubernetes in 
optimizing resource utilization, reducing latency, and improving throughput. Additionally, the 
implementation of ZTA is validated through metrics indicating significant reductions in 
unauthorized access attempts and high authentication success rates. The results underscore the 
framework's potential to address key challenges in AI-driven applications, offering a secure, 
scalable, and high-performance solution for personalized decision-making in multi-cloud 
environments. This study provides insights into the practical application of advanced AI 
technologies and contributes to the advancement of secure and efficient AI systems.