Securing Java Applications on AWS with ML-Driven Access Control

Static IAM policies cannot stop credential misuse because valid credentials look legitimate. We built a ML-driven access control system using AWS SageMaker and Java Spring Security. Results include 92% accuracy on known attacks and 81% detection of credential misuse with sub-100ms latency. This article shares architecture diagrams and code examples for implementing behavioral baselines and anomaly detection in production.