Integrating DevSecOps into Large-Scale Cloud Migration Projects: Challenges, Strategies, and Emerging Best Practices for 2025

Approved

Relevance and Originality:

This research addresses a pressing challenge in the realm of digital transformation by focusing on the integration of DevSecOps into large-scale cloud migration. As organizations increasingly rely on cloud platforms to improve scalability, agility, and cost-efficiency, the importance of embedding security from the ground up becomes critical. The study’s emphasis on AI-driven threat detection and continuous security integration reflects current trends and presents a novel contribution. By focusing on 2025 best practices and outcomes, the work stands out in its relevance and originality within both academic and industry contexts.

Methodology:

The mixed-methods approach strengthens the study by combining qualitative interviews with industry experts and quantitative data from diverse cloud migration case studies. This methodology enables a comprehensive understanding of DevSecOps strategies, uncovering how automated compliance checks, cross-functional collaboration, and early security integration influence project success. The design is well-suited to exploring the complex interplay of technical, procedural, and cultural factors in cloud security during migration.

Validity & Reliability:

The reported 40% reduction in security incidents and 30% improvement in deployment speed provide strong evidence for the effectiveness of DevSecOps when integrated from the onset of migration projects. The consistency of these results across varied sectors enhances their reliability. While additional detail on sample diversity would further support the generalizability of the findings, the combination of empirical metrics and expert validation contributes to the robustness and practical relevance of the conclusions.

Clarity and Structure:

The Research Article is organized with clarity and maintains a logical progression from problem statement to solution framework. The discussion on continuous security, compliance automation, and the impact of machine learning is presented in a way that balances technical depth with readability. Complex ideas are articulated clearly, allowing both technical professionals and decision-makers to grasp the implications without ambiguity. This clarity enhances the accessibility and impact of the research.

Result Analysis:

The analysis effectively ties the integration of DevSecOps practices to measurable gains in security and operational performance. The study highlights the strategic role of AI and machine learning in enhancing detection and response, and emphasizes the importance of early-stage planning and collaboration. The findings underscore how embedding security into the cloud migration lifecycle contributes to stronger postures and smoother deployment workflows.