Hybrid AI-Oriented DevSecOps Architecture for Intelligent Multi-Cloud Enterprise Platforms
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P132Keywords:
DevSecOps, Artificial Intelligence, Multi-Cloud Computing, Cloud Security, Intelligent Automation, Kubernetes, CI/CD Pipeline, Autonomous Remediation, Hybrid Cloud, Enterprise PlatformsAbstract
The rapid adoption of cloud-native technologies, artificial intelligence (AI), and distributed enterprise computing has significantly transformed modern software engineering and infrastructure management practices. Organizations increasingly depend on multi-cloud ecosystems to achieve scalability, resilience, flexibility, and business continuity. However, conventional DevOps and security management frameworks often struggle to address the complexity of heterogeneous cloud infrastructures, intelligent automation requirements, real-time cyber threat detection, and continuous compliance monitoring. In this context, Artificial Intelligence-enabled DevSecOps has emerged as a strategic paradigm capable of integrating intelligent analytics, autonomous security orchestration, predictive monitoring, and adaptive deployment optimization within modern enterprise environments. This research proposes a Hybrid AI-Oriented DevSecOps Architecture specifically designed for intelligent multi-cloud enterprise platforms. The proposed architecture combines AI-driven analytics, automated security pipelines, continuous integration and continuous deployment (CI/CD), intelligent orchestration engines, observability frameworks, and autonomous remediation mechanisms across hybrid cloud infrastructures. The study evaluates how machine learning algorithms, predictive analytics, anomaly detection systems, and automated policy enforcement mechanisms improve deployment reliability, security posture, operational efficiency, and infrastructure scalability. The research methodology employs a conceptual architectural modeling approach combined with comparative analysis, cloud security evaluation, and intelligent workflow integration strategies. The proposed model integrates Kubernetes orchestration, Infrastructure-as-Code (IaC), AI-driven threat intelligence systems, zero-trust security policies, and intelligent observability frameworks to enhance operational governance in enterprise cloud ecosystems. Furthermore, the study compares traditional DevOps frameworks with AI-oriented DevSecOps approaches using performance indicators such as deployment speed, threat mitigation efficiency, fault recovery time, infrastructure adaptability, and resource optimization. The results demonstrate that AI-oriented DevSecOps significantly enhances automation efficiency, predictive maintenance capability, cyber resilience, and operational intelligence in multi-cloud environments. The proposed architecture reduces security vulnerabilities, accelerates incident response processes, and improves infrastructure adaptability while supporting continuous compliance and intelligent workload orchestration. The study contributes a scalable research framework for future enterprise cloud operations and establishes a foundation for autonomous cloud-native security engineering. The findings are highly relevant for researchers, enterprise architects, cloud engineers, and cybersecurity professionals working in intelligent enterprise computing systems.
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