Multi-Cloud Threat Modeling with Reinforcement Learning: A New Frontier in Enterprise Defense
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Abstract
As enterprises increasingly adopt multi-cloud architectures to enhance scalability, resilience, and cost-efficiency, the complexity of securing these environments grows exponentially. Traditional threat modeling approaches often fall short in addressing the dynamic and distributed nature of multi-cloud infrastructures. This paper presents a novel framework for Multi-Cloud Threat Modeling using Reinforcement Learning (RL) to predict, identify, and mitigate potential vulnerabilities in real-time. By leveraging RL's adaptive learning capabilities, the proposed model continuously evolves based on threat patterns, system configurations, and cloud interactions, offering proactive defense mechanisms. This approach not only improves detection accuracy but also optimally allocates security resources across heterogeneous cloud platforms, minimizing response times and reducing attack surfaces. Experimental results demonstrate significant improvements in threat detection and mitigation compared to conventional methods, making this RL-based model a pioneering step toward robust multi-cloud security
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References
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