Predictive AI-Driven Intelligent API Analytics for Resilient Enterprise Cloud Transformation and Advanced Cybersecurity

Abstract

Predictive Artificial Intelligence (AI) is increasingly transforming enterprise cloud environments by enabling intelligent monitoring, automated decision-making, proactive threat detection, and resilient digital transformation. Application Programming Interfaces (APIs) have become essential components of modern enterprise ecosystems because they connect cloud platforms, microservices, enterprise applications, data services, and external digital partners. However, the rapid growth of API-driven architectures has also expanded cybersecurity risks, operational complexity, performance challenges, and governance requirements. This research proposes a Predictive AI-Driven Intelligent API Analytics framework for resilient enterprise cloud transformation and advanced cybersecurity. The framework integrates machine learning, predictive analytics, anomaly detection, behavioral intelligence, real-time API monitoring, automated threat response, and cloud-native orchestration. Historical and real-time API telemetry, including request patterns, latency, authentication events, error rates, traffic volume, and security logs, are analyzed to identify abnormal behavior and predict potential failures or cyberattacks before significant damage occurs. The proposed methodology combines data collection, preprocessing, feature engineering, predictive model development, anomaly detection, risk scoring, and automated response mechanisms. The framework supports enterprise resilience by improving service availability, reducing incident response time, strengthening Zero Trust security, and enabling intelligent cloud resource optimization. The study demonstrates that predictive API analytics can provide a proactive foundation for secure, scalable, adaptive, and autonomous enterprise cloud transformation.