Enhancing Cloud-Native Cyber Defense using Deep Learning for Predictive Threat Intelligence and Adaptive Incident Response

Abstract

The rapid adoption of cloud-native technologies has transformed modern information technology by enabling organizations to develop, deploy, and manage applications using microservices, containers, Kubernetes orchestration, and serverless computing. While these technologies improve scalability, flexibility, and operational efficiency, they also introduce increasingly sophisticated cybersecurity challenges that traditional security approaches struggle to address. Cyber threats targeting cloud-native environments continue to evolve, requiring intelligent, proactive, and adaptive defense mechanisms capable of identifying attacks before they cause significant damage. Deep learning has emerged as a promising solution for predictive threat intelligence because of its ability to analyze vast volumes of structured and unstructured security data, identify hidden attack patterns, and forecast potential cyber threats. Furthermore, adaptive incident response powered by artificial intelligence enables automated detection, prioritization, containment, and remediation of security incidents with minimal human intervention. This essay explores the integration of deep learning techniques into cloud-native cybersecurity frameworks to strengthen predictive threat intelligence and adaptive incident response capabilities. It reviews existing literature, identifies research gaps, and proposes a comprehensive research methodology for designing and evaluating an intelligent cloud-native cyber defense framework. The proposed approach aims to improve threat prediction accuracy, response efficiency, system resilience, operational scalability, and cybersecurity decision-making while reducing incident response time and enhancing protection against continuously evolving cyber threats