AI-Enhanced Devops Frameworks For Automated Security And Continuous Delivery In Cloud-Native Systems
Abstract
The convergence of DevOps practices with artificial intelligence (AI) and cloud-native architectures represents a transformative evolution in contemporary software engineering. Organizations increasingly require frameworks that harmonize agility, reliability, and security while managing the complexities of distributed systems, microservices, and continuous delivery pipelines. This research explores the integration of AI-driven mechanisms within DevOps to automate vulnerability management, patch deployment, and demand forecasting, thereby optimizing operational efficiency and reducing exposure to cyber threats. Through an exhaustive review of seminal literature and empirical studies, the paper identifies key dimensions of DevOps, including culture, automation, measurement, and sharing, and investigates how AI interventions can augment these dimensions to enable predictive, real-time security management. The methodology synthesizes theoretical and practical insights from canonical texts, cloud deployment frameworks, microservices observability tools, and AI-driven security systems to propose a comprehensive conceptual model for secure, automated, and intelligent CI/CD pipelines. Findings indicate that AI-enhanced DevOps frameworks significantly improve patching efficiency, reduce system downtime, facilitate intelligent orchestration of resources, and enhance the overall security posture of cloud-native applications without impeding delivery velocity. This research contributes both theoretically and practically by delineating pathways for integrating AI within DevOps pipelines, highlighting operational limitations, and proposing future directions, including adaptive orchestration, standardized observability protocols, and governance models for hybrid and containerized cloud environments.
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References
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