Adaptive DevSecOps: Integrating AI-Driven Threat Detection in Continuous Delivery Pipelines

Abstract

DevOps and Continuous Delivery (CD) have transformed the landscape of software development at a rapid rate, creating new security concerns that cannot be effectively resolved by older approaches. DevSecOps became a paradigm shift or model in which security was inserted into the software development process. Nevertheless, the growing complexity of microservices, cloud-native environments, and the changing threat environments all require more flexible and smarter solutions. The paper suggests an adaptive DevSecOps model powered by AI that incorporates real-time threat detection in CI/CD pipelines and allows for the prevention and mitigation of security threats proactively, without affecting the speed of development. We propose a system that integrates static and dynamic analysis that uses machine learning, behavioural anomaly detection and continuous learning techniques to secure pipelines. Model training and inference occur with logs, code repositories, and configuration data as input, and the model trains over time via feedback loops. The effectiveness and feasibility of the suggested approach have been confirmed by the experiments, which achieved extremely high detection accuracy (up to 97 percent) with low false positive rates and minimal latency. Real-world case study demonstrates system scalability and responsiveness in real-life scenarios. The integration issues, ethics, and challenges of adoption in the industry are also mentioned in the paper. Lastly, it presents research directions to be pursued in the future, which lie within the areas of explainability, adaptive response orchestration, and federated learning in collaborative threat inte