About This Architecture

Zeek SOC AI Detection Pipeline on AWS integrates on-premise Zeek sensors with a multi-AZ AWS infrastructure for real-time network threat detection and machine learning-driven analysis. Traffic flows from Zeek sensors through WAF and ALB to backend EC2 instances, which queue detection tasks to AI Worker instances for model inference against logs and evidence stored in S3. RDS PostgreSQL with cross-AZ replication, ElastiCache for performance, and comprehensive monitoring via CloudWatch, GuardDuty, and Security Hub provide high availability, data durability, and security visibility. This architecture demonstrates defense-in-depth with encryption via KMS, secrets management, and automated alerting to SOC analysts. Fork and customize this diagram on Diagrams.so to adapt subnet ranges, instance types, or add additional detection layers.

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How do I design a scalable, highly available SOC detection pipeline on AWS that integrates Zeek sensors with AI-powered threat analysis?

This diagram shows a production-grade architecture where Zeek sensors send network traffic through AWS WAF and ALB to backend EC2 instances, which queue detection tasks to AI Worker instances for machine learning inference. RDS PostgreSQL with cross-AZ standby ensures data durability, while CloudWatch, GuardDuty, and Security Hub provide real-time monitoring and alerting to SOC analysts.