AI-Augmented DevSecOps for Cloud Environments: Bridging Security Gaps in Modern Continuous Delivery Pipelines
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806035Keywords:
AI, DevSecOps, Cloud Security, Continuous Delivery, Machine Learning, CI/CD Pipelines, Threat Detection, Automation, Vulnerability Management, CybersecurityAbstract
The rapid adoption of cloud computing and continuous delivery pipelines has transformed how software is developed, deployed, and maintained. However, this transformation has introduced significant security challenges, including misconfigurations, vulnerabilities in third-party dependencies, and delayed threat detection. AI-augmented DevSecOps emerges as a powerful approach to address these issues by embedding intelligent security mechanisms across the software development lifecycle. This paper explores how artificial intelligence enhances DevSecOps practices in cloud environments by enabling proactive threat detection, automated vulnerability management, and adaptive security policies. AI models can analyze large volumes of code, logs, and system behaviors in real time, identifying anomalies that traditional tools often miss. Furthermore, integrating AI into CI/CD pipelines strengthens compliance enforcement and reduces human error. The study examines existing frameworks, tools, and methodologies, highlighting their role in bridging security gaps without compromising delivery speed. Challenges such as model bias, data privacy concerns, and implementation complexity are also discussed. Ultimately, AI-driven DevSecOps represents a paradigm shift toward resilient, scalable, and secure cloud-native development practices, enabling organizations to balance agility with robust security in increasingly complex digital ecosystems.
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