Next-Generation AI-Driven Cloud Reliability and DevSecOps Optimization Frameworks
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P126Keywords:
Artificial Intelligence, Cloud Reliability Engineering, DevSecOps, Intelligent Automation, Cybersecurity, Zero-Trust Security, Predictive Analytics, Explainable AI, Cloud-Native Infrastructure, Enterprise ReliabilityAbstract
The rapid expansion of cloud-native infrastructures, distributed enterprise systems, containerized applications, and intelligent digital ecosystems has transformed the operational foundations of modern enterprises. Organizations increasingly rely on scalable cloud platforms and continuous software delivery pipelines to support mission-critical services, real-time analytics, and global digital operations. However, the growing complexity of multi-cloud architectures, cybersecurity threats, software vulnerabilities, operational failures, and dynamic workload fluctuations has exposed significant limitations in conventional cloud reliability engineering and DevSecOps practices. Traditional monitoring and security mechanisms often struggle to provide adaptive, intelligent, and autonomous operational resilience across heterogeneous enterprise environments. In response to these challenges, next-generation Artificial Intelligence (AI)-driven cloud reliability and DevSecOps optimization frameworks have emerged as transformative technological paradigms capable of enabling intelligent infrastructure orchestration, predictive reliability management, adaptive cybersecurity enforcement, and autonomous operational decision-making. AI-driven frameworks integrate machine learning, deep learning, reinforcement learning, explainable AI, federated learning, and intelligent automation technologies into cloud reliability engineering and DevSecOps ecosystems. These intelligent frameworks enhance system observability, predictive maintenance, workload optimization, anomaly detection, automated threat mitigation, policy compliance monitoring, and continuous deployment reliability. This research article presents a comprehensive investigation of next-generation AI-driven cloud reliability and DevSecOps optimization frameworks through detailed academic analysis, comparative evaluation, and conceptual framework development. The study examines the integration of AI technologies into cloud reliability engineering, cybersecurity operations, infrastructure orchestration, CI/CD automation, and zero-trust security architectures. Furthermore, the research identifies critical research gaps associated with explainability, interoperability, scalability, governance, adversarial AI risks, and ethical AI adoption. The proposed conceptual framework integrates intelligent monitoring systems, predictive analytics engines, autonomous orchestration modules, AI-powered security enforcement, and explainable governance mechanisms to support secure and resilient cloud-native enterprise operations. The findings demonstrate that AI-driven reliability and DevSecOps frameworks significantly improve infrastructure resilience, operational efficiency, cyber defense capabilities, resource optimization, and software deployment stability. However, the study also highlights challenges related to computational overhead, governance complexity, AI trustworthiness, and integration across heterogeneous cloud environments. This article contributes to the academic and industrial understanding of AI-enabled enterprise cloud reliability and DevSecOps optimization by providing comprehensive technical analysis, implementation insights, research gap identification, and future strategic directions suitable for next-generation intelligent enterprise ecosystems.
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