Threat Hunt AI: Building an AI Security Analyst on AWS for Under $500/Month

Grow Therapy's engineering team details how they built a Claude-powered automated threat-hunting system for their healthcare platform, ingesting 15 log sources (AWS, Okta, GitHub, CrowdStrike, Slack, etc.) into Snowflake and Datadog. 

The system runs 20 daily AI hunts via ECS Fargate tasks, with a multi-phase analysis pipeline: data gathering (Sonnet), baseline comparison, context enrichment, confidence scoring, and adversarial validation—where the model actively argues against its own findings using historical false-positive patterns and actor history to reduce noise. 

This last phase cut false positives by ~40%. The system costs $320–510/month, leveraging existing observability infrastructure, with a feedback loop allowing analysts to mark false positives that feed back into future hunts via frequency-based filtering. 

Key lessons: data pipeline matters more than the AI, build feedback loops before deploying, tune hunts in staging mode to find the right signal-to-noise ratio, start with narrow tool access, and track token usage from day one. The result is proactive, daily security coverage across all critical systems at a fraction of traditional SOC costs. 

https://engineering.growtherapy.com/post/threat-hunt-ai-how-we-built-an-ai-security-analyst-on-aws-for-under-500-month

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