International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2024
p-ISSN: 2395-0072
www.irjet.net
Multi-Cloud Data Strategy & Security for Generative AI Chaitanya Vootkuri Distinguished Cloud Security Architect, USA ------------------------------------------------------------------------***------------------------------------------------------------------------Abstract: The rapid growth of generative artificial intelligence has fundamentally changed the requirements for cloud computing infrastructure, including requisites such as innovative approaches to resource management and development strategies. A multi-cloud strategy involves leveraging multiple cloud providers to execute an application to optimize data management, storage, and processing capabilities for training and inference. This comprehensive research paper aims to study the evolving paradigm of multi-cloud strategies tailored for Generative Artificial intelligence (Gen-AI) using the multi-cloud platforms to enhance their infrastructure, reliability, and security and how the costs are optimized by effectively reducing vendor lock-ins and provide a chance to strategically leverage a variety of providers and their skills to meet specific company demands. The paper demonstrates multi cloud data strategy and security frameworks for Gen AI applications. The research discusses how to protect GenAI using different strategies in enterprise ecosystems.
Keywords: Multi-cloud strategy, Cloud computing services, Generative AI, Cloud providers, Computational capabilities, Performance optimization, cloud architecture, Hybrid cloud, Cloud Security.
1. INTRODUCTION The emergence of Generative AI has catalyzed an unprecedented transformation in cloud computing infrastructure requirements, creating unique challenges that traditional single-cloud deployments struggle to address effectively. As organizations worldwide rapidly adopt and deploy increasingly sophisticated GenAI applications, the demands on computing resources, data management capabilities, and cost control mechanisms have grown exponentially. Recent market analysis indicates that the GenAI market reached $13.4 billion in 2023 and is projected to expand to $67.3 billion by 2027. This explosive growth has been accompanied by computing resource demands that double every 3.4 months, pushing data center GPU utilization rates above 95% in significant regions (Johnson & Lee, 2024). Traditional single-cloud approaches need to be revised to address these challenges, leading organizations to explore multi-cloud strategies as a solution. These strategies enable organizations to leverage the unique strengths of different cloud providers while mitigating their limitations, creating more robust and efficient infrastructure frameworks for GenAI deployments (Zhang & Williams, 2023). The complexity of GenAI workloads, combined with various regional regulations, data sovereignty requirements, and performance optimization needs, has made multi-cloud approaches beneficial and often necessary for successful large-scale deployments.
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Figure1: A picturization of a multi-cloud environment
2. BACKGROUND Evolution of Multi-Cloud Architecture The evolution of multi-cloud data strategy has progressed significantly, advancing from basic redundancy models to sophisticated, AI-driven architectures. In the 2010–2015, multi-cloud strategies centered on VM-based deployments, emphasizing basic redundancy and inter-cloud connectivity to mitigate vendor lock-in risks. Between 2016 and 2019, the focus shifted to container-based orchestration, with technologies like Kubernetes and software-defined networking (SDN) enabling optimized workload distribution across cloud environments. Since 2020, multicloud strategies have integrated AI-optimized infrastructure and cloud-native service meshes, enabling intelligent workload distribution and advanced automation. This progression empowers organizations to
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