AI to Detect and Mitigate Security Vulnerabilities in APIs: Encryption, Authentication, and Anomaly Detection in Enterprise-Level Distributed Systems

Abstract

APIs represent the foundation in every enterprise-class distributed system while enabling interaction, data exchange, or interoperability amongst diverse applications and services. However, ease of accessibility and their critical role make them vulnerable to security perils that could be disastrous in terms of integrity and performance of enterprise infrastructures if exploited. While encryption, multi-factor authentication, and rule-based anomaly detection are essential layers of security, their in-built limitations and lack of flexibility or adaptiveness place barriers on the prevention of sophisticated, evolving cyber threats. AI brings important improvements to the detection and mitigation of API vulnerabilities with real-time, data-driven security insights and adaptive responses. The paper addresses how to use AI in view of API security challenges on encryption, authentication, and anomaly detection. It investigates some AI approaches, including machine learning models that grade encryption strength, adaptive algorithms that measure consistency in authentication, and deep anomaly detection systems aimed at finding deviations in API traffic patterns. These AI-driven solutions support a multilayered security strategy that enhances more traditional approaches and facilitates a more responsive and robust security framework appropriate for the dynamic demands of large-scale distributed systems. While AI does not replace existing security practices, its deployment is a strategic enhancement, offering continuous and context-aware assessments that can help safeguard enterprise APIs against evermore sophisticated threats.