Securing the Multi-Cloud Frontier: An AI-Driven Adaptive Framework for Real-Time Threat Detection and Autonomous Response
Author : Fousiya PU
Abstract :
The rapid proliferation of multi-cloud architectures has fundamentally transformed enterprise IT infrastructure while simultaneously introducing unprecedented security challenges. Existing solutions—whether provider-native tools, third-party security platforms, or AI-driven detection systems—consistently fail to bridge the critical gap between real-time threat detection and autonomous adaptive response across heterogeneous cloud environments. This paper proposes the Adaptive Multi-Cloud Security Framework (AMCSF), a novel architecture comprising three tightly integrated components: a Policy Translation Engine (PTE) for cross-cloud policy harmonization, an Ensemble Threat Detection (ETD) system combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, and a Self-Evolving Response Orchestrator (SERO) employing reinforcement learning for autonomous countermeasure deployment. The framework is designed and validated using Design Science Research Methodology (DSRM) within simulated multi-cloud environments spanning Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Expected outcomes include detection accuracy exceeding 95%, a false positive rate below 5%, mean time to detect under one minute, and an automated response rate above 70%. AMCSF directly addresses five identified research gaps—unified detection-response integration, cross-cloud policy harmonization, real-time adaptability, cross-cloud learning, and scalable deployment—thereby delivering both theoretical advancement and practical implementation guidance for securing heterogeneous cloud ecosystems.
Keywords :
Multi-Cloud Security, Adaptive Framework, Artificial Intelligence, Threat Detection, Autonomous Response, Reinforcement Learning, Policy Harmonization, Deep Learning.
