Securing Container Images through Automated Vulnerability Detection in Shift-Left CI/CD Pipelines

Integrating shift-left security practices and automated vulnerability detection in container images is imperative for modern software development, given the dynamics and vulnerability landscape. This crucial methodology emphasizes security from the initial stages of integration in container-based environments like Docker and Kubernetes. The paper examines containerization security challenges, including image vulnerabilities, insecure configurations, runtime risks, weak orchestration security, and supply chain weaknesses, while stressing compliance with regulatory rules. It explores how this automated approach leverages vulnerability detection methods integrated into Continuous Integration/Continuous Deployment (CI/CD) pipelines through static and dynamic analyses, vulnerability databases, and policy-enforcement mechanisms. Beyond identifying vulnerabilities in CI/CD pipelines, the paper outlines methods to avoid policy violations, mitigate vulnerable images, and prevent recurring practices. Importantly, it underscores the continuous enforcement and remediation of policies and security standards. Security teams must invest efforts in developing policies, automated executions, and remediation procedures, fostering cross-departmental collaboration. In essence, this proactive stance aims to enhance software security, reduce risks, and improve adherence in containerized ecosystems, making it an indispensable component of modern software development.

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Publication Details

Journal
Babylonian Journal of Networking
Published
2024-08-20
DOI
https://doi.org/10.58496/bjn/2024/016
Citations
8
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
2.80
Controls
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article

Securing Container Images through Automated Vulnerability Detection in Shift-Left CI/CD Pipelines

Pushan Kumar Dutta, Arvind Kumar Bhardwaj, Pradeep Chintale
8 citations
Babylonian Journal of Networking
Advanced Malware Detection Techniques
2.80
article

Securing Container Images through Automated Vulnerability Detection in Shift-Left CI/CD Pipelines

Pushan Kumar Dutta, Arvind Kumar Bhardwaj, Pradeep Chintale
article en
8 citations

Abstract

Integrating shift-left security practices and automated vulnerability detection in container images is imperative for modern software development, given the dynamics and vulnerability landscape. This crucial methodology emphasizes security from the initial stages of integration in container-based environments like Docker and Kubernetes. The paper examines containerization security challenges, including image vulnerabilities, insecure configurations, runtime risks, weak orchestration security, and supply chain weaknesses, while stressing compliance with regulatory rules. It explores how this automated approach leverages vulnerability detection methods integrated into Continuous Integration/Continuous Deployment (CI/CD) pipelines through static and dynamic analyses, vulnerability databases, and policy-enforcement mechanisms. Beyond identifying vulnerabilities in CI/CD pipelines, the paper outlines methods to avoid policy violations, mitigate vulnerable images, and prevent recurring practices. Importantly, it underscores the continuous enforcement and remediation of policies and security standards. Security teams must invest efforts in developing policies, automated executions, and remediation procedures, fostering cross-departmental collaboration. In essence, this proactive stance aims to enhance software security, reduce risks, and improve adherence in containerized ecosystems, making it an indispensable component of modern software development.

Babylonian Journal of NetworkingVol. 2024
University of Houston - Downtown (US), Houston Independent School District (US)
Openalex Percentile: Top 9%
Advanced Malware Detection Techniques
2.80
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Securing Container Images through Automated Vulnerability Detection in Shift-Left CI/CD Pipelines — Pushan Kumar Dutta, Arvind Kumar Bhardwaj, et al. · Babylonian Journal of Networking (2024) | TGRS Research Map | TGRS