ADAPTIVE API RATE CONTROL AND SECURITY ENFORCEMENT FOR HIGH-VOLUME DIGITAL SERVICES

Abstract

High-volume digital services rely extensively on Application Programming Interfaces (APIs) to deliver scalable communication among cloudnative applications, microservices, mobile platforms, Internet of Things devices, and enterprise systems. As API traffic continues to increase, conventional static rate-limiting mechanisms become insufficient for handling dynamic workloads, sophisticated cyberattacks, and fluctuating service demands. This paper proposes an adaptive API rate control and security enforcement framework integrating OpenAPI Specification, machine learningassisted traffic analytics, cloud-native DevSecOps, Kubernetes orchestration, API gateways, Zero-Trust security, and continuous enterprise monitoring. The proposed methodology dynamically adjusts API rate limits based on runtime behavioral analysis while simultaneously enforcing authentication, authorization, anomaly detection, and automated security policies. Experimental evaluation demonstrates improvements in traffic management, cyberattack mitigation, API availability, operational scalability, governance efficiency, and enterprise resilience. The proposed framework provides a scalable, intelligent, and production-ready solution for securing high-volume digital services operating within modern cloud-native enterprise environments. Keywords— API Rate Control, API Security, Runtime Analytics, Machine Learning, OpenAPI Specification, DevSecOps, Cloud-Native Computing, Enterprise Integration.