AI and Machine Learning for API Security

This research paper explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing Application Programming Interface (API) security. It focuses on real-time API monitoring, anomaly detection, behavioral analysis, threat identification, risk classification, and automated security responses. The study reviews six existing research works and proposes an AI-driven security framework that classifies incoming API requests into three categories: safe, suspicious, and malicious. Based on the identified risk level, the proposed system aims to allow legitimate requests, verify suspicious activities, and block potentially harmful requests while maintaining security logs for further analysis. The paper also highlights key challenges, including false alarms, data quality, computational requirements, and adapting to evolving cyber threats. It presents a conceptual framework and future evaluation approach to support the development of more intelligent, adaptive, and reliable API security systems.