Abstract

AI-Enabled DevSecOps Frameworks for Continuous Security Validation in Cloud-Native Applications examines the integration of artificial intelligence into DevSecOps practices to enable continuous, automated, and proactive security validation throughout the software development lifecycle. Cloud-native applications increasingly rely on microservices, containers, Kubernetes platforms, APIs, serverless components, and continuously changing infrastructure, creating complex security challenges that traditional periodic security assessments may not adequately address. AI-enabled DevSecOps frameworks can integrate security analysis into development, build, deployment, and runtime stages by combining machine learning, intelligent vulnerability detection, behavioral analysis, threat identification, and automated risk assessment. Such frameworks can analyze source code, dependencies, container images, infrastructure-as-code configurations, application behavior, and runtime telemetry to identify potential vulnerabilities and misconfigurations. Machine learning models can assist in prioritizing security findings based on contextual risk, reducing alert fatigue and enabling development teams to focus on high-impact issues. The framework can also incorporate continuous feedback from security testing, operational incidents, and newly identified threats to improve detection capabilities over time.