Articles | Open Access |

Embedding Security into the Pipeline: A Framework for Scalable DevSecOps Implementation

Gaurav Malik , SAP America Inc., USA

Abstract

The security use of DevSecOps is becoming very important to ensure the applications are secure throughout their life cycle, as the adoption of DevSecOps continues to rise. By incorporating security into the entire software development life cycle, DevSecOps can allow for more secure code at the earliest stage, thus minimizing vulnerabilities. This paper explores current challenges and innovations in DevSecOps, focusing on how scalable security integration is open to discussion. It outlines the most relevant obstacles, including the ineffectiveness of the cooperation between the teams of security and developers, challenges of choosing compatible tools related to security, and the security culture that should be shifted toward putting security on the development stage early. The study points out the significance of automated security testing, monitoring around the clock, and advanced security solutions that are an inseparable part of the development chain. The future research in the area of this study is aimed at increasing automation and integration of AI, modifying security procedures to work with a cloud-native architecture and microservices, and improving anomaly detection techniques. The study also suggests the need to close the skills gap in companies and the creation of efficient metrics to measure the effectiveness of DevSecOps implementations. The results contribute to the need for organizations to embrace security as a collective responsibility, enhancing the release cycles without compromising security. DevSecOps frameworks are evolving to become a decisive factor in the creation of secure, scalable, and effective software applications against the rising cybersecurity threats.

Keywords

Automation, Scalability, AI Integration, Behavioral Analytics, Security as Code

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Embedding Security into the Pipeline: A Framework for Scalable DevSecOps Implementation. (2024). International Journal of Data Science and Machine Learning, 4(01), 37-62. https://www.academicpublishers.org/journals/index.php/ijdsml/article/view/6120