AI Driven ChatOps with for Realtime Security Incident Response in DevSecOps

Abstract— DevSecOps techniques speed up software delivery, while security incident response occasionally lags in speed and integration. This study provides an artificial intelligence-powered ChatOps framework to streamline and automate security incident response inside DevSecOps procedures. We demonstrate how ChatOps (chat-centric operations) and artificial intelligence (AI) enable real-time automatic remediation, cooperative investigation, and quick detection of security issues. The technique integrates machine learning-based threat detection with team chat platforms (like Microsoft Teams and Slack), allowing security bots to use chat instructions to perform containment operations, assist in triage, and notify teams of incidents with more information. We evaluate the benefits of this integration, such as improved teamwork, less human error, faster reaction times, and continuous event learning. In a case study of the banking sector, automated documentation demonstrates a significant improvement in compliance and reaction speed (minutes as opposed to hours). We discuss topics like automation confidence, false positives, and tool integration, and we suggest future advancements like advanced language models for incident handling. The results demonstrate how ChatOps, powered by AI, might transform incident response into a rapid, proactive process that aligns with DevSecOps agility. Keywords— DevSecOps, ChatOps, AI in Cybersecurity, Security Automation, Incident Response, Security Orchestration Automation and Response (SOAR), Machine Learning, Security Bots, Real- Time Threat Detection, Natural Language Processing, Collaboration Tools, Continuous Monitoring, SIEM, Microsoft Sentinel, IBM QRadar, GPT-powered Assistants, Financial Technology, Cyber Threat Intelligence.