AI-powered incident analysis for Amazon RDS using automated forensic artifacts

This article demonstrates a serverless approach to continuously collect database forensic artifacts from RDS/Aurora instances and use AI tools to perform rapid root cause analysis of database incidents.

• Automatically capture point-in-time snapshots of database internals (sessions, locks, wait statistics, queries) on a recurring schedule and store in S3
• Use AI assistants like Kiro with S3 and CloudWatch access to correlate artifacts and perform root cause analysis in seconds
• Serverless architecture uses EventBridge, Lambda, SQS, and Secrets Manager to collect diagnostic data from SQL Server, PostgreSQL, MySQL, Oracle, and DocumentDB
• Solution captures complete database state including active sessions, blocking chains, transaction log status, and tempdb usage
• Example analyses show identifying tempdb pressure, uncommitted transactions blocking log truncation, and capacity planning trends
• Security uses least-privilege IAM, VPC deployment, encrypted S3 storage, and Secrets Manager for credentials

By continuously collecting forensic artifacts, organizations can transform database incident investigation from hours of manual work into instant AI-assisted analysis, reducing mean time to resolution.