AI-Powered Incident Response Framework for Secure DevOps Environments

Contemporary DevOps systems are also under constant pressure to keep their systems reliable, to identify anomalies and to react to security incidents on the fly. The nature of infrastructure and the pace of deployment render manual incident response ineffective and fraught as organizations become more dependent and more reliant on constant integration and delivery pipelines. The current solutions, including Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and the random forest-based models, have been implemented in incident prediction and management, and face limitations related to the lack of ability to perform the predictions across a variety of different operational contexts, sluggishness in reaching the convergence and the low level of the adaptability to evolving security threats. In order to address these issues, this paper will introduce an AI-Powered Incident Response Framework of Secure DevOps Environments (AIRF-SD) to predict, prioritize, and mitigate incidents using reinforcement learning and intelligent automation. The suggested model puts forward the incident response process as a Markov Decision Process (MDP) and uses a reward based Q-learning approach to dynamically optimalize decisions in accordance with operational condition. It is tested on a synthesized dataset of DevOps incidents and the performance of the system is compared with baseline models such as DQN, PPO and Random Forest. It has been shown that AIRF-SD has an accuracy of 96.4%, precision of 0.95, recall of 0.96, and an AUC of 0.97, and is faster to converge and less time consuming to execute than any baseline. These deliverables affirm the efficiency of the suggested framework in proactive, adaptive, and reliable incident response of secure DevOps operations.