AI-Driven Frameworks for Enhancing Cybersecurity in MultiCloud Environments

Abstract

The proliferation of multi-cloud architectures has transformed the digital landscape, enabling organizations to optimize workloads, reduce operational costs, and improve scalability. However, this paradigm shift has also introduced complex cybersecurity challenges, including fragmented security policies, expanded threat surfaces, and heterogeneous infrastructure vulnerabilities. This paper proposes a comprehensive AI-driven cybersecurity framework designed specifically for multi-cloud environments. Leveraging machine learning techniques such as anomaly detection, behavior analysis, and threat intelligence automation, the framework provides adaptive defense mechanisms that evolve with emerging threats. Deep learning models are integrated for real-time anomaly detection across diverse cloud providers, while reinforcement learning algorithms facilitate continuous policy optimization. The study also incorporates a case-based simulation using hybrid cloud platforms to evaluate the effectiveness of the proposed system in detecting advanced persistent threats (APTs), phishing attacks, and insider threats. Results demonstrate a significant improvement in threat detection accuracy, response time, and policy enforcement consistency. The findings support the integration of artificial intelligence as a critical enabler for robust, scalable, and intelligent cybersecurity strategies in multi-cloud ecosystems.