{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/ab966bb76ebd4c44bef1ec6b0c906fdc\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/ab966bb76ebd4c44bef1ec6b0c906fdc-a81216352557ed84.gif","duration":209.061,"title":"Autonomous Cloud Compliance &amp; AI Auditing Engine — AWS + n8n + Langflow + Groq + Supabase","description":"In this video I walk you through an autonomous cloud security and compliance auditing system I architected from scratch — designed to replace hours of manual AWS infrastructure review with a fully automated, AI-powered pipeline.\nWhat it does:\n\nExtracts metadata from 10+ AWS services like S3, RDS, EC2, IAM, Lambda, CloudTrail, KMS, and more\nRoutes every finding through a SWITCHBOARD that classifies results as Pass, Fail, or Manual Review\nStores all audit results in Supabase as a persistent, queryable audit trail\nRuns the full infrastructure data through a Groq + Llama 3 AI agent in Langflow to generate a 200-point security assessment\nDelivers a downloadable compliance report ,ready to hand off to any team\n\nTech used: n8n · Langflow · Groq · AWS CLI · Docker · Python/Flask · Supabase · LLM Prompt Engineering\nThe problem it solves: What used to take an engineering team hours of manual work ,this system completes in under two minutes."}