AI-Enhanced Microservice Security in Cloud-Based Financial Platforms: A Case Study of AWS Implementation

Abstract

Securing cloud-based financial platforms presents unique challenges in an era of increasing cyber threats and distributed architectures. This article introduces a novel framework integrating artificial intelligence with microservices security, specifically designed for insurance and annuity sector applications. The proposed solution leverages predictive analytics and machine learning algorithms to enhance threat detection and response capabilities within containerized environments, implemented using Kubernetes on AWS infrastructure. The article demonstrates significant improvements in threat detection accuracy, system response time, and overall security posture while maintaining operational efficiency. The framework's implementation across multiple financial institutions reveals enhanced capabilities in identifying zero-day threats, reducing false positives, and maintaining regulatory compliance. Results indicate substantial improvements in security incident response times and operational efficiency compared to traditional security approaches. This article contributes to the growing body of knowledge in cloud security by presenting a scalable, AI-driven security architecture that addresses the unique challenges of microservices in financial platforms, while providing a blueprint for future implementations in similar high-security environments.