• Shyam Srikant Bharadwaj
• T Harika
• U Jayanth
• Dr. Rakshitha Kiran P

Current DevSecOps pipelines often struggle with inefficiencies because they run complete security scans on every commit. This leads to longer runtimes, unnecessary tests, and higher false positive rates. To address these problems, this paper introduces an adaptive scheduler based on mathematical functions that intelligently decides which security scans are actually needed. The scheduler uses scoring, thresholding, optimization, and probability-based models to choose only the necessary scans. It combines SAST, DAST, IaC, dependency, and container scanning tools, cutting down extra costs and resource use while maintaining good quality. The system also improves resistance to evasion and false positives, achieving about a 40% reduction in runtime. The result is a scalable, auditable, and efficient DevSecOps pipeline that can be applied across enterprises and regulated sectors. This approach represents the next generation of secure CI/CD pipelines.

Adaptive Security Scheduling, Mathematical Decision Engine, Probabilistic Scan Selection, Bayesian Risk Modeling, Optimization-Based Pipeline Security, Policy-as-Code Framework, Forensic-Aware DevSecOps, AI-Augmented Security Testing, Context-Aware Vulnerability Detection, Compliance- Driven Adaptive Testing

"Adaptive_Security_Testing_Scheduler_for_DevSecOps_Pipelines", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.f313-f319, November-2025, Available :http://www.jetir.org/papers/JETIR2511557.pdf

"Adaptive_Security_Testing_Scheduler_for_DevSecOps_Pipelines", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppf313-f319, November-2025, Available at : http://www.jetir.org/papers/JETIR2511557.pdf