INTEGRATING AI/ML INTO DEVSECOPS: STRENGTHENING SECURITY AND COMPLIANCE IN CLOUD-NATIVE APPLICATIONS

Abstract

Cloud-native application security in contemporary enterprise settings can be revolutionized by incorporating Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps procedures. This thorough examination looks at how businesses may improve security testing automation, vulnerability identification, and compliance monitoring across the development lifecycle by utilizing AI/ML capabilities. Organizations report up to 76% fewer false positives, 88% higher threat detection accuracy, and 71% quicker vulnerability remediation, according to the study, which is based on substantial industry research and implementation data. While tackling important issues like model training quality and integration complexity, the study also looks at workable frameworks for integrating AI-driven security controls in CI/CD pipelines, Infrastructure as Code (IaC), and real-time threat detection systems. This analysis shows how AI/ML integration may significantly improve security operations while lowering manual involvement and increasing development velocity through thorough case studies and performance indicators.