Cloud Native Security Architecture with Deep Learning Based Intelligent Anomaly Detection and Enterprise Threat Response

Abstract

Cloud-native computing has transformed enterprise information technology by enabling organizations to build, deploy, and scale applications through containers, microservices, serverless platforms, orchestration systems, and distributed cloud infrastructure. However, the dynamic and highly interconnected nature of cloud-native environments introduces complex security challenges, including rapidly changing workloads, expanded attack surfaces, identity-based threats, lateral movement, zero-day attacks, misconfigurations, and sophisticated application-layer intrusions. Conventional rule-based security mechanisms frequently struggle to identify previously unseen or evolving threats in such environments. This essay proposes a cloud-native security architecture integrating deep-learning-based intelligent anomaly detection with enterprise threat response capabilities. The proposed approach combines continuous telemetry collection, identity and access monitoring, network-flow analysis, container and workload monitoring, security information and event management, and threat-intelligence enrichment within a unified security architecture. Deep learning models are employed to learn complex behavioral patterns and identify deviations associated with malicious or abnormal activities. An intelligent response layer subsequently prioritizes detected incidents, correlates evidence, evaluates risk, and initiates automated or human-supervised remediation. The methodology emphasizes scalable data processing, feature engineering, model training, validation, real-time inference, explainability, and continuous adaptation. The proposed architecture aims to improve detection accuracy, reduce false positives, accelerate incident response, and strengthen enterprise resilience while preserving the scalability and agility that characterize cloud-native computing