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Intelligent DevSecOps Transformation through Reinforcement Learning and Predictive Observability for Enterprise CI/CD Pipelines

Dr. Mabruk Fekihal

Faculty of Computing & Information Technology Sohar University, Oman

Abstract

The increasing complexity of enterprise software delivery environments has created a need for DevSecOps approaches that can continuously optimize security, performance, reliability, and delivery efficiency. Conventional CI/CD pipelines typically rely on predefined workflows, static quality gates, manually configured resource allocation, and reactive monitoring. Although these mechanisms provide consistency, they may not respond effectively to changing workloads, evolving security risks, application dependencies, or infrastructure conditions. This study proposes an intelligent DevSecOps transformation framework that integrates Reinforcement Learning (RL) with predictive observability to enable adaptive optimization of enterprise CI/CD pipelines. The proposed framework collects and correlates metrics, logs, traces, security findings, deployment events, test results, infrastructure telemetry, and historical pipeline information to construct a predictive operational view. Machine-learning-based forecasting and anomaly detection identify potential failures, performance degradation, security risks, and resource constraints before they significantly affect delivery. An RL agent subsequently evaluates the observed pipeline state and selects constrained optimization actions, including test prioritization, resource allocation, deployment strategy selection, security validation intensity, and rollback initiation. A multi-objective reward function balances delivery speed, reliability, security, resource efficiency, and recovery performance. The research methodology employs controlled experiments comparing the proposed approach with conventional rule-based DevSecOps and non-adaptive optimization methods. Evaluation focuses on deployment lead time, pipeline failure rate, change failure rate, security detection, resource utilization, recovery time, prediction accuracy, and operational cost. The study aims to demonstrate a practical pathway toward predictive, adaptive, and secure enterprise software delivery

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0806028

Pages

15541-15549

Published

December 20, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr. Mabruk Fekihal (%2025). Intelligent DevSecOps Transformation through Reinforcement Learning and Predictive Observability for Enterprise CI/CD Pipelines. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15541-15549. https://doi.org/10.15662/IJSRAT.2025.0806028

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