AI-DRIVEN API SECURITY: ARCHITECTING RESILIENT GATEWAYS FOR HYBRID CLOUD ECOSYSTEMS

Abstract

Application Programming Interfaces (APIs) have become the backbone of modern hybrid cloud ecosystems, enabling interoperability, data exchange, and business agility. However, this ubiquity also makes APIs a prime target for cyberattacks such as credential stuffing, data exfiltration, distributed denial of service (DDoS), and business logic abuse. Traditional API gateways and Web Application Firewalls (WAFs) rely heavily on static rules, signatures, and policy-based enforcement, which often fail against zero-day exploits and adaptive adversarial techniques. To address these gaps, this paper proposes an AI-driven API security architecture that embeds machine learning (ML) and anomaly detection models within API gateways to provide resilience, scalability, and adaptive threat prevention in hybrid cloud environments. The architecture integrates federated learning, real-time threat intelligence, and selfhealing mechanisms to reduce mean-time-to-detect (MTTD) and mean-time-to-recover (MTTR) while ensuring compliance with data privacy and regulatory mandates. A comparative evaluation highlights the superiority of AI-driven gateways in accuracy, resilience, and performance trade-offs compared to conventional methods. This work contributes a practical blueprint for enterprises seeking to architect secure, futureready API ecosystems in the era of hybrid and multi-cloud adoption.