Unified AI and ML Framework in DevSecOps Practices, Solving Real-World Problems

Artificial Intelligence (AI) and Machine Learning (ML) are evolving daily and advancing human progression in every field of endeavor like arts, science, and business. Information Technology (IT) is the field where this transformation is most accelerated due to its role in enabling digital transformation across other industries. DevSecOps is the standard set of practices that emerged as the ideal solution for managing the software development life cycle in this digital transformation journey. DevSecOps practices are also evolving along with the IT industry, and there are challenges and opportunities to improve continuously. This research paper introduces a conceptual framework for implementing a unified AI and ML solution to optimize the DevSecOps practice of any organization and constantly improve. This framework aims to solve some of the most challenging problems in DevSecOps practices by accelerating software development, reducing defects, increasing security scanning accuracy, enhancing automation, improving performance testing, and streamlining operations. Our solution accomplishes this using consolidated DevSecOps data-driven predictive analytics output from the Unified AI and ML model. By introducing this conceptual framework, the paper encourages more research and development in the core area of DevSecOps practices, which function as the cornerstone of digital transformation across all industries.