Enhancing CI/CD Pipelines and Container Security Through Machine Learning and Advanced Automation

EasyChair Preprint 15622

Abstract

The evolution of Continuous Integration/Continuous Deployment (CI/CD) pipelines has transformed software development practices by enabling rapid and reliable delivery. However, the increasing reliance on containers and the complexity of modern pipelines have introduced new challenges in security, efficiency, and scalability. This paper explores the integration of Machine Learning (ML) and advanced automation techniques to address these challenges. Through dynamic risk assessment, intelligent anomaly detection, and proactive security measures, ML can significantly enhance CI/CD workflows. Advanced automation further optimizes processes, reducing human error and accelerating delivery timelines. This research emphasizes the convergence of ML and automation as a transformative approach to strengthening CI/CD pipelines and container security, providing insights for developers, security professionals, and organizations seeking innovative solutions.

Keyphrases: CI/CD Pipelines, Container Security, machine learning

@booklet{EasyChair:15622,
  author    = {Atika Nishat},
  title     = {Enhancing CI/CD Pipelines and Container Security Through Machine Learning and Advanced Automation},
  howpublished = {EasyChair Preprint 15622},
  year      = {EasyChair, 2024}}