AUTOMATED API SECURITY VALIDATION THROUGH OPENAPI SPECIFICATIONS AND MACHINE LEARNING

Abstract

Application Programming Interfaces (APIs) have become the primary communication mechanism for cloud-native enterprise applications, making API security validation essential for protecting enterprise systems against evolving cyber threats. Conventional security testing techniques are often manual, time-consuming, and incapable of continuously validating rapidly changing APIs throughout modern DevSecOps pipelines. This paper proposes an automated API security validation framework integrating OpenAPI Specifications, machine learning, DevSecOps, cloud-native deployment, intelligent vulnerability detection, continuous compliance monitoring, and enterprise governance. The proposed methodology automatically analyzes API specifications, identifies security weaknesses, validates authentication and authorization mechanisms, performs contractbased security testing, and continuously monitors deployed APIs for policy violations. Experimental evaluation demonstrates improvements in vulnerability detection accuracy, API security, governance efficiency, deployment reliability, and operational scalability. The proposed framework provides a production-ready and intelligent solution for automated API security validation in modern enterprise integration environments. Keywords— API Security, OpenAPI Specification, Machine Learning, DevSecOps, API Validation, Cloud-Native Applications, Enterprise Security, Automated Testing.