International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 7 | Year 2025 | Article Id. IJCTT-V73I7P112 | DOI : https://doi.org/10.14445/22490183/IJCTT-V73I7P112

Securing DevOps Pipelines in the Age of AI: Comparative Insights and Best Practices

Aseem Mankotia, Rohith Chinnaswamy, Sara Venkatachalam

Received Revised Accepted Published
07 Jun 2025 29 Jun 2025 20 Jul 2025 30 Jul 2025

Citation :

Aseem Mankotia, Rohith Chinnaswamy, Sara Venkatachalam, "Securing DevOps Pipelines in the Age of AI: Comparative Insights and Best Practices," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 7, pp. 98-100, 2025. Crossref, https://doi.org/10.14445/22490183/IJCTT-V73I7P112

Abstract

Thus, it becomes necessary to understand how Artificial Intelligence (AI) works as part of DevOps practices, as the two are fast merging. Therefore, this work aims to explain exactly what AI is in the context of DevOps and investigate ways of building ethical and explainable software pipelines. Thus, the application of AI entails enormous prospects for automating the processes and increasing the efficiency of activities throughout the DevOps life cycle; at the same time, the use of AI generates critical ethical questions. This paper thus discusses the operation of AI in the DevOps process and how it is applied in the development, testing, deployment, and monitoring of applications. It also explores contentious issues like the fairness of using AI in DevOps and interpretability/algorithmic transparency. Therefore, the paper unveils the integration of DOI with AI while admitting that the fourth industrial revolution technology can automate job tasks and augment the learning process and the performance, development, and growth of domain knowledge and expertise. The transition from software licensing counterpart to SaaS and the advantages of fast and frequent software release are described. The integrated method of DevOps, along with the deployment of technologies like big data, cloud, and mobile internet, calls for speed in the delivery of software and consistency in integration and delivery. Areas like cost-based analysis of the products, automated production analysis, and control are reviewed with ample use of AI in the automation and diagnosis of software and hardware products. Altogether, the paper describes AI-optimized DevOps as one of the effective, high-velocity models of development and deployment with the help of case studies, best practices, and examples. It also investigates AIOps and MLOps usage jointly with DevOps practices to address the chasm between machine learning (ML) model creation and operationalization. Finally, this research’s goal is to arm practitioners and organizations with the information and strategies required to handle the present and anticipated advancements in AI-associated DevOps sustainably and openly in the cloud CI/CD system and today’s software development context.

Keywords

DevOps, Continuous Integration/Continuous Deployment (CI/CD), AI in DevOps, Pipelines, AIOps.

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