AI-Driven Cloud-Native Enterprise Systems Leveraging Kubernetes, DevSecOps, and Predictive Analytics
DOI:
https://doi.org/10.15662/IJARCST.2023.0602001Keywords:
Artificial Intelligence, Cloud-Native Systems, Kubernetes, DevSecOps, Predictive Analytics, Enterprise Architecture, Containerization, Microservices, Machine Learning, Continuous Integration, Continuous Deployment, Cybersecurity, Digital Transformation, Cloud Computing, Intelligent AutomationAbstract
The rapid evolution of digital transformation has encouraged organizations to adopt cloud-native architectures that provide scalability, resilience, agility, and operational efficiency. Artificial Intelligence (AI), Kubernetes orchestration, DevSecOps practices, and predictive analytics have emerged as critical enablers for next-generation enterprise systems. AI-driven cloud-native enterprise systems integrate intelligent automation, continuous security, and data-driven decision-making within highly distributed computing environments. Kubernetes serves as the foundational orchestration platform that automates deployment, scaling, and management of containerized applications across hybrid and multi-cloud infrastructures. DevSecOps extends traditional DevOps methodologies by embedding security controls throughout the software development lifecycle, ensuring compliance, risk mitigation, and rapid delivery of secure applications. Predictive analytics leverages machine learning algorithms and large-scale data processing to forecast system behavior, identify anomalies, optimize resource allocation, and support strategic business decisions. The convergence of these technologies enables enterprises to build adaptive, resilient, and intelligent digital ecosystems capable of responding dynamically to changing business requirements and cybersecurity threats. This essay explores the conceptual foundations, technological advancements, and organizational implications of AI-driven cloud-native enterprise systems. It examines existing literature, analyzes the role of Kubernetes and DevSecOps in enabling secure cloud-native environments, and presents a comprehensive research methodology for investigating the effectiveness of predictive analytics in enterprise operations. The study contributes to understanding how integrated cloud-native technologies can enhance operational performance, security posture, innovation capacity, and sustainable competitive advantage in modern enterprises
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