Securing Cloud-Native Microservices Using AI-Driven Threat Detection Models

Abstract

Modern enterprise systems are now based on cloud-native architectures relying on microservices, containers, and orchestration systems, such as Kubernetes. Despite the scalability, resilience, and agility of microservices, they expand the attack surface which exposes the cloud-native applications to advanced threats. In such environments, traditional rule-based security systems are unable to keep up with dynamic load distribution, zero-day attacks and distributed attack vectors. Threat-detection solutions with Artificial Intelligence (AI) have become a promising area to secure microservices using machine learning (ML), deep learning (DL), and anomaly detection models. The present paper provides a review of AI-based threat detection models in microservices on the cloud that cover supervised, unsupervised, and reinforcement learning methods. It discusses the major applications including intrusion detection, API traffic anomaly detection, container runtime protection, and workload behavior analysis. The AI-powered systems will improve the detection rates, false positives, and provide dynamic immunity to new cyber threats. The paper also identifies the implementation framework using AI with service meshes, observability tools, and Security Information and Event Management (SIEM) systems. Fintech, healthcare, and e-commerce case studies show the feasibility of AI-based detection in practice in cloud-native settings. In spite of those developments, there are still issues related to data quality, explainability, model drift and adherence to privacy requirements. The article highlights the importance of explainable AI (XAI), federated learning to achieve collaborative defense, and combining with zero-trust architecture. Microservice systems that combine predictive AI models with autonomous response systems are self-healing microservice systems of the future. With resilient, adaptive, and trustworthy cloud-native applications, organizations can stay in the era of more advanced cyberattacks by securing microservices with AI-driven threat detection.