The rapid digital transformation of retail enterprises, driven by cloud adoption and microservices architectures, has intensified the need for robust security frameworks that ensure compliance, operational resilience, and resistance to emerging threats. Traditional DevOps practices, while effective for accelerating delivery, often lack sufficient mechanisms for ensuring security throughout the software development lifecycle. In response, the DevSecOps paradigm integrates security practices directly into DevOps workflows, shifting security left and embedding continuous verification within continuous integration and continuous deployment (CI/CD) pipelines. This research article examines the theoretical foundations, empirical practices, and emerging innovations in DevSecOps, with an emphasis on the retail cloud domain where compliance and resilience are particularly critical due to sensitive customer data and regulatory constraints. Drawing on established literature and recent advances, including strategies for secure DevOps in retail cloud environments (Gangula, 2025), this work synthesizes a comprehensive understanding of how security tools, machine learning, and systemic organizational strategies coalesce to strengthen cloud security.

The study critically engages with themes such as automated security verification, machine learning integration, microservices intrusion detection, and secure development methodologies. By weaving insights from systematic reviews, architectural proposals, and methodology papers, the research illuminates the multifaceted challenges and practical solutions in contemporary DevSecOps adoption. Findings suggest that while automation and AI/ML tools provide substantial gains in threat detection and compliance monitoring, organizational culture, metrics frameworks, and model‑based security design play indispensable roles. Furthermore, limitations remain in adequately securing dynamic microservices environments and aligning DevSecOps practices with evolving regulatory frameworks. The article concludes by proposing future research directions focused on adaptive security frameworks, enhanced interpretability of AI models, and cross‑domain integrations capable of addressing emerging cyber threats in retail cloud infrastructures.

DevSecOps, cloud security, retail cloud

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