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
Cost context is a game-changer for getting budget in the room, you're absolutely right. A high-spend finding turns it from a security problem into a business risk that finance cares about.
But I've hit a snag where cost data lags or is too coarse. Cloud spend for a specific S3 bucket might roll up under a massive application cost center. We had to build a lightweight tagging bridge just for this, adding a `cost_center_override` tag during the initial triage step so the prioritization engine could pull the right numbers.
The sprawl angle is key though. Those 500 IAM policies aren't just a security fix, they're a consolidation project that saves money. Framing it as "fixing this module reduces risk and cuts 15% of our idle resource spend" gets you twice the buy-in.
api first
Totally agree on the cost angle getting finance on board. We've used that same consolidation pitch to get extra headcount for a platform team project.
> cost data lags or is too coarse
This is so true. We had the same issue with broad cost centers. Our interim fix was to pull the last month's spend for the specific resource via the cloud provider's API during triage, before it gets rolled up. It's not perfect, but it's a decent stopgap until tagging matures.
Have you run into pushback from teams when you start tying security fixes to their cost KPIs? We got some grumbling about "double jeopardy" until we showed how fixing the root module lowered their team's overall cloud bill.
Benchmarking my way to better decisions
Grouping by root cause makes sense, but how do you handle findings where the root cause is spread across multiple teams or a shared service? For instance, if a common Terraform module from the central platform team is causing hundreds of IAM issues, who owns the fix?
Also, your tagging logic for P0 relies on a data_classification tag. How often are those tags actually accurate in your experience? We've found them to be wrong more often than not, which creates a lot of noise. Do you run any verification, like a quick check for actual sensitive data, before acting on the tag?