AI Driven ChatOps for DevSecOps: Automating Security Incident Response

ABSTRACT: Though security incident response sometimes trails in speed and integration, DevSecOps approaches hasten software delivery. This study offers a ChatOps platform driven by artificial intelligence to automate and simplify security incident response inside DevSecOps processes. We show how artificial intelligence (AI) combined with ChatOps (chat-centric operations) allows fast detection, collaborative investigation, and automated remediation of security problems in real time. The method puts machine learning-based threat detection into team chat systems (e.g. Slack, Microsoft Teams), hence enabling security bots to alert teams of incidents with enhanced context, help in triage, and carry out containment operations using chat commands. We assess the advantages of this integration, including quicker reaction times, better teamwork, less human error, and ongoing incident learning. Automated documentation in a banking industry case study shows notable response speed (minutes rather than hours) and compliance improvement. We talk about issues such tool integration, false positives, and confidence in automation and recommend future improvements such sophisticated language models for incident management. The findings show that ChatOps driven by artificial intelligence may turn incident response into a proactive, quick procedure in line with DevSecOps agility.