Cybersecurity Risk Modeling in CI/CD Pipelines Using Reinforcement Learning for Test Optimization

Incremental software development and deployment brought about the much-advertised Continuous Integration and Continuous Deployment (CI/CD) approaches that have changed completely how modern applications are constructed, tested, and launched. But the fast-delivery strategy hugely opened the gates to cyber threats, giving CI/CD pipelines the status of most-sought cyber-hacking targets. Traditional static security models have been frequently experienced to fail in in line with the dynamic nature of CI/CD workflows, hence allowing undetected vulnerabilities to persist and prolonging remediation. This study proposes the utilization of reinforcement learning (RL) for optimizing cybersecurity risk modeling and testing in CI/CD pipelines. The system makes maximum use of real-time threat intelligence, in combination with dynamic test selection techniques, toward maximum detection of vulnerabilities within the smallest possible amount of resource allocation. RL agents are trained to always push severe test scenarios first in a way to better absorb changing attacks and codebase dynamics. Empirical study results show improved detection rates, less test time, and better risk visibility in all stages of the pipeline, marking a major fight toward intelligent and adaptive DevOps security practices.