AI-Powered Security for Microservices and Container Platforms: A Review Analysis

Abstract

The rapid adoption of microservices architecture and containerized platforms has transformed the development, deployment, and management of modern cloud-native applications. However, their distributed, dynamic, and ephemeral nature introduces significant security challenges, including unauthorized access, API attacks, anomalous behaviour, resource exploitation, and distributed denial-of-service attacks. Traditional rule-based and static security mechanisms often struggle to detect evolving and previously unknown threats in such environments. This review analyzes the application of Artificial Intelligence (AI) for strengthening security across microservices and container platforms. It examines microservices architecture, containerization technologies, orchestration platforms, service mesh technologies, and their associated security challenges. The study further reviews AI-based security approaches, including machine learning, deep learning, anomaly detection, intrusion detection, adaptive API protection, and intelligent resource management. A taxonomy of anomaly detection based on metrics, logs, and traces is presented to highlight different observability perspectives. Recent studies are comparatively analyzed according to their methodologies, findings, limitations, and future directions. The review identifies AI’s potential to enable adaptive, scalable, and proactive security while highlighting challenges related to computational cost, data availability, model complexity, false positives, and real-time deployment. Future research should focus on explainable, lightweight, and autonomous AI-driven security frameworks.