ENTERPRISE AI AT SCALE: ARCHITECTING SECURE MICROSERVICES WITH SPRING BOOT AND AWS

Abstract

This article presents a novel blueprint for architecting microservice ecosystems using Spring Boot and AWS to deploy AI-driven predictive models at scale. Leveraging ECS, Lambda, S3, and Step Functions, the framework integrates seamlessly with machine learning APIs for real-time recommendations. A unique security layer using KMS, Secrets Manager, and OAuth SSO ensures compliance with enterprise-grade cybersecurity protocols. The study evaluates response times, model accuracy, and breach resilience across financial applications. A case implementation in retail banking illustrates how infrastructure-native AI services can be securely integrated into production using developer-friendly tooling. The proposed architecture achieves 99.97% uptime, sub-200ms response times for AI predictions, and demonstrates 45% improvement in model deployment velocity while maintaining SOC 2 Type II compliance standards.