Integrating Machine Learning into the Security of Containerized Workloads

Abstract

Containers and containerized workloads have revolutionized the software development lifecycle by enabling faster development cycles, better resource utilization, and seamless integration with DevOps and CI/CD pipelines. They also facilitate efficient cloud migration and multi-cloud deployments. However, the dynamic and ephemeral nature of containers introduces new and evolving security risks, such as runtime threats, misconfigurations, and vulnerabilities in the supply chain. Traditional security mechanisms often fall short in these highly dynamic environments. This study investigates the integration of machine learning (ML) techniques into container security frameworks to detect anomalies, predict potential threats, and automate response mechanisms. By analyzing behavioral patterns in containerized environments, ML enhances threat detection accuracy, reduces response time, and improves overall system resilience. The findings highlight the potential of ML-driven solutions to proactively safeguard container ecosystems in real-time.