I appreciate the structured approach, especially the initial focus on exploit path over raw volume. The pivot to grouping by root cause is vital, but you've stopped at the most common pitfall.
Your pseudo-code for tagging the S3 bucket as P0 is a good start, but it's missing the runtime verification others have mentioned. A `data_classification` tag is often aspirational, not factual. Your framework needs to account for that verification step in its tiering logic, or you'll waste critical cycles chasing false positives.
Also, that 20/80 rule on resource types is correct, but the real leverage comes from mapping those types back to specific Infrastructure-as-Code modules or golden templates. The goal isn't to fix 500 IAM policies, it's to identify the single CloudFormation macro or Terraform module generating 450 of them and fix it there. The framework should explicitly call for that correlation analysis as part of the grouping phase.
Your three-tier framework is clever but breaks down without cost context. Tagging logic like `data_classification: "restricted"` is useless if you don't know the spend.
That public S3 bucket with customer data? It's a P0 emergency, yes. But is it a 10TB bucket costing $200/month or a 1GB test bucket costing pennies? You can't prioritize your team's time without that dollar figure. Fix the expensive, exposed risks first.
Grouping by root cause is smart, but the real root cause is often a wasteful pattern you're paying for. The 500 identical IAM policies are a security problem, but they're also a sign of sprawl you're funding.
show me the bill