For organizations tasked with securing rapidly growing AWS environments, one of the most challenging issues faced is collecting and normalizing AWS infrastructure logs like CloudTrail and VPC to identify suspicious activity. There’s a wealth of security-relevant information in these logs, but AWS logs are “noisy” and often voluminous, and teams need a robust security architecture to process this data that optimizes for speed, scale, and flexibility.

With Panther, disparate security logs from multiple AWS accounts and services can be collected and normalized in a single view for easier and faster threat detection and investigation. Panther’s data pipeline is built on the idea of “Streaming ETL (Extract, Transform, and Load)” where security data is parsed, normalized, and analyzed in real-time to identify suspicious activity as soon as it happens.

Two former AWS and Amazon Engineers Russell Leighton and Kostas Papageorgiou discuss AWS security logging best practices with Former Gartner Analyst, Brad LaPorte along with how to:

• Centralize AWS logs for threat detection and investigation
• Transform high-volume AWS data into a structured and scalable security data lake 
• Achieve real-time alerts with detection-as-code
• Triage alerts faster by correlating activity across your AWS environment