Cloud Native Risk Detection Using Deep Learning for Continuous Enterprise Cyber Defense and Security Monitoring

Abstract

Cloud-native computing has transformed enterprise information technology by enabling scalable, distributed, and highly dynamic application environments through containers, Kubernetes, microservices, serverless platforms, and automated deployment pipelines. However, these environments introduce complex security risks because workloads, identities, APIs, configurations, and network relationships continuously change. Traditional security monitoring approaches often struggle to process the scale, velocity, and complexity of cloud-native telemetry. This research proposes a deep learning-based framework for continuous enterprise cyber defense and security monitoring that integrates heterogeneous cloud-native security data with intelligent risk detection and adaptive threat analysis. The proposed framework collects telemetry from containers, Kubernetes clusters, APIs, applications, identities, networks, endpoints, and cloud infrastructure and applies preprocessing, feature engineering, behavioral analysis, and deep learning techniques to identify anomalous and potentially malicious activities. Recurrent neural networks, autoencoders, and other deep learning architectures are considered for temporal behavior modeling, anomaly detection, and classification of security events. A contextual risk engine combines model predictions with asset criticality, identity privileges, vulnerability information, threat intelligence, and historical security events to prioritize risks. Continuous feedback mechanisms support model adaptation to changing workloads and emerging threats. The methodology evaluates detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, and response efficiency. The proposed approach is intended to improve continuous visibility, reduce alert overload, strengthen proactive threat detection, and support resilient cloud-native enterprise cyber defense.