AGENTIC AI-BASED SECURE MULTI-CLOUD ENVIRONMENT WITH HOMOMORPHIC ENCRYPTION AND ADAPTIVE BANDWIDTH OPTIMIZATION

Abstract

The proliferation of multi-cloud architectures has introduced significant security and performance challenges for enterprise organizations. This research presents a novel framework integrating agentic AI with homomorphic encryption and adaptive bandwidth optimization to address these challenges. Traditional multi-cloud environments struggle with data security during computation and inefficient resource allocation across heterogeneous cloud platforms. Our approach employs autonomous AI agents that dynamically manage encrypted data processing while optimizing bandwidth allocation based on real-time network conditions and workload characteristics. The framework implements fully homomorphic encryption (FHE) to enable secure computation on encrypted data without decryption, eliminating vulnerability windows during processing. Concurrently, AI agents continuously monitor network performance metrics and adjust bandwidth allocation to minimize latency and maximize throughput. Through experimental validation across AWS, Azure, and Google Cloud Platform, we demonstrate that our framework achieves 43% reduction in data exposure risk, 37% improvement in bandwidth utilization efficiency, and maintains computational overhead within acceptable limits of 15-18% compared to unencrypted operations. The agentic approach proves particularly effective in handling dynamic workload variations, automatically redistributing computational tasks and network resources without human intervention. This research contributes both to cloud security literature and practical implementation guidance for organizations seeking to leverage multi-cloud strategies without compromising data protection or performance.