Building a Secure DevOps Pipeline: The Role of Automated Threat Detection and Response in Multi-Cloud Environments
Keywords:
DevOps, multi-cloud, automated threat detection, response systems, hybrid models, machine learning, security.Abstract
In the fast-evolving landscape of cloud computing, maintaining the security of applications within a DevOps pipeline has become increasingly complex, particularly in multicloud environments. This paper investigates the role of automated threat detection and response mechanisms in securing DevOps pipelines. With the advent of tools and practices such as Continuous Integration (CI), Continuous Delivery (CD), and Infrastructure as Code (IaC), ensuring robust security throughout the pipeline is paramount. This paper builds on insights from recent research on data-driven models, specifically utilizing hybrid models like those presented in A Novel Fusion Deep Learning Approach for Retinal Disease Diagnosis Enhanced by Web Application Predictive Tool by Banik et al. (2023), to demonstrate how predictive tools, enhanced by automation, can improve security. The research shows the benefits of fusion deep learning models in identifying and mitigating threats dynamically. We will explore how integrating automated security measures and threat detection systems into the pipeline can prevent vulnerabilities before deployment.