Vol. 10 No. 5 (2026): International Research Journal of Innovative Engineering (IRJIE)

Cloud-native software development has transformed enterprise application delivery by emphasizing automation, scalability, continuous integration, continuous deployment, containerization, and distributed infrastructure. However, increasing application complexity, frequent releases, heterogeneous cloud environments, and growing security requirements create challenges for conventional CI/CD pipelines. This study proposes an intelligent cloud-native DevOps automation framework that integrates machine learning, automated testing, predictive failure analysis, security intelligence, container orchestration, and adaptive deployment strategies to improve enterprise software delivery resilience. The proposed framework continuously analyzes source-code changes, build outcomes, test results, infrastructure telemetry, deployment events, and historical pipeline behavior to identify potential failures before production release. Intelligent decision mechanisms dynamically optimize pipeline execution, resource allocation, test prioritization, deployment sequencing, and rollback actions. The architecture integrates automated quality gates, DevSecOps controls, infrastructure-as-code, container orchestration, observability, and policy-driven governance within a unified CI/CD environment. Experimental evaluation considers deployment frequency, lead time, pipeline success rate, failure recovery time, change failure rate, test execution efficiency, resource utilization, security findings, and rollback effectiveness. Comparative analysis with conventional CI/CD pipelines is used to determine the contribution of intelligent automation and predictive analytics. The proposed approach aims to reduce deployment failures, accelerate software delivery, strengthen security, and improve operational resilience. By combining cloud-native engineering practices with intelligent pipeline automation, the framework provides a scalable foundation for reliable, secure, and adaptive enterprise software delivery.

The increasing adoption of cloud-based data platforms has created significant challenges for enterprises seeking to maintain data quality, security, consistency, and regulatory compliance across distributed information environments. Artificial Intelligence (AI)-enabled data governance offers opportunities to address these challenges through automated metadata management, intelligent data classification, anomaly detection, and real-time analytical monitoring. This research examines an integrated approach to cloud data governance using SAP Datasphere as a centralized environment for data integration, semantic modeling, and enterprise-wide data accessibility. The proposed framework combines metadata management capabilities with AI-driven analytics to improve data discovery, lineage tracking, quality assessment, access control, and governance decision-making. Metadata describing data ownership, business definitions, transformations, sensitivity, and usage patterns provides the contextual foundation for intelligent governance processes. Real-time analytics further enables organizations to identify emerging data quality issues, detect unusual access patterns, and monitor compliance-related indicators. The research methodology adopts a quantitative, design-oriented approach involving framework development, experimental implementation, and comparative evaluation against conventional governance practices. Evaluation criteria include metadata completeness, data quality, anomaly detection accuracy, lineage transparency, compliance monitoring efficiency, and analytical response time. The study aims to establish how AI and metadata-driven governance can improve enterprise data reliability, operational transparency, and responsible data utilization. The proposed approach contributes to the development of scalable, adaptive, and trustworthy cloud data governance practices within modern data-driven organizations

The increasing adoption of enterprise cloud computing has introduced complex cybersecurity challenges associated with dynamic workloads, distributed infrastructures, identity management, and rapidly evolving attack techniques. Conventional security mechanisms primarily focus on detecting ongoing or completed attacks, limiting their ability to anticipate emerging threats and prevent incidents before significant damage occurs. This study proposes a deep learning-based framework for security event forecasting and proactive incident prevention in enterprise cloud operations. The framework integrates cloud audit logs, network traffic records, authentication events, workload telemetry, vulnerability information, and historical incident records to identify temporal patterns associated with potential security incidents. Deep learning architectures, including Long Short-Term Memory networks, Gated Recurrent Units, temporal convolutional networks, and transformer-based models, are considered for forecasting security events and detecting abnormal behavioral sequences. The proposed methodology incorporates data preprocessing, temporal feature extraction, predictive modeling, risk estimation, and preventive response prioritization. Model performance is evaluated using precision, recall, F1-score, forecasting error, false-alarm rate, and warning lead time. Additional considerations include computational efficiency, concept drift, data privacy, and operational scalability. By transforming historical and real-time security telemetry into forward-looking risk indicators, the proposed framework aims to improve threat anticipation, reduce incident response delays, and support evidence-based preventive actions. The study establishes a methodological foundation for integrating predictive intelligence into enterprise cloud security operations while emphasizing reliable evaluation, human oversight, and controlled automation