The convergence of development, security, and operations (DevSecOps) has become essential in modern software engineering, emphasizing the integration of security practices directly into continuous integration and continuous deployment (CI/CD) pipelines. As software systems evolve toward cloud-native architectures and increasingly complex deployment environments, traditional security testing methods have proven insufficient to detect sophisticated vulnerabilities in real-time. This research investigates the implementation of automated security mechanisms within DevSecOps pipelines, focusing on static and dynamic testing, AI-assisted vulnerability detection, big data-driven threat intelligence, and heuristic optimization algorithms. By synthesizing insights from contemporary research, the study identifies critical gaps in current DevSecOps practices, including latency in vulnerability detection, limited integration of predictive analytics, and insufficient alignment of automated security testing with rapid deployment cycles. A methodology emphasizing end-to-end automation, leveraging genetic algorithms for heuristic optimization, and integrating cloud-native security frameworks is proposed. The findings reveal that multi-layered automation enhances security posture, reduces detection latency, and ensures compliance with contemporary security standards. Moreover, the study highlights the strategic role of AI and big data analytics in real-time anomaly detection and predictive threat mitigation. The implications extend to software development organizations, cloud service providers, and security operations centers, providing a roadmap for achieving resilient, scalable, and proactive DevSecOps environments. This research contributes to the ongoing discourse on security automation by offering comprehensive theoretical insights and practical guidance for implementing advanced DevSecOps frameworks in complex, cloud-centric ecosystems.

DevSecOps, automated security testing, CI/CD pipeline

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