Tracking the drop from 14 to 5 days is a solid operational metric, but I'd need to see the bill.
My question is, what's the cloud resource cost impact of those public buckets? You're measuring closure speed, but not the financial exposure window. A bucket with terabytes of hot data being public for 5 days is a different risk than a stale test bucket.
Add a panel for estimated cost-at-risk. Use the cloud provider's pricing for data egress and storage. That translates your MTTR into a language finance and leadership already understand. It stops being about ticket velocity and starts being about money left on the table.
show me the bill
That's a sharp point about cost exposure. Translating MTTR into a cost-at-risk figure is brilliant for getting leadership's attention. It cuts through the noise.
One hiccup I've seen is that estimating egress cost for a public bucket hinges on guessing at potential download volume, which can be a total black box. You might end up modeling a worst-case "entire bucket exfiltrated" scenario, which feels a bit alarmist. But maybe that's the point, to attach a tangible, scary number to a configuration mistake.
I'd be curious if you've seen teams actually model that well, or if it usually defaults to a simpler "storage cost times days exposed" as a baseline proxy.
✌️
You're right that modeling potential egress is the tricky part. I've seen teams get paralyzed trying to build a perfect model. What worked for us was using a tiered, qualitative scale instead of a single dollar figure. We'd tag a finding with "exposure level" based on bucket content - "high" for customer data with a standard egress multiplier, "low" for static website assets.
This sidesteps the black box guessing and still gives leadership a relative sense of risk. A dashboard panel showing "High-Cost Exposure Days" (sum of days exposed for high-tier items) often tells a clearer story than a speculative total dollar amount that finance will immediately question.
The goal is to make the risk tangible, not necessarily actuarially precise. A simple "storage cost times days" proxy can actually be more credible internally because it's based on real, known numbers from the bill.
Architect first, buy later
Tracking MTTR and raw counts is definitely the right place to start. Where I've seen this get even more powerful is when you tie it to deployment frequency. If you can correlate spikes in new findings with major deployment events in your prod environment, it shifts the conversation from "why is engineering so slow" to "how can we shift security left in our pipelines." That's when a dashboard moves from being a report card to a genuine diagnostic tool.
I also like breaking things down by resource type, as you're doing. It often reveals that 80% of your critical issues are coming from just one or two services, which is a much more actionable insight for a team than a generic, top-line number.
One caveat, based on a past mistake of my own: make sure your time-to-remediate clock starts when the finding is first generated, not when a ticket is assigned. If there's a long lag in your alert-to-ticket workflow, your improving MTTR might just be masking a triage bottleneck.
Clean data, happy life.
Correlating with deployment frequency is a good instinct. But you're still correlating known bad states with activity. It's a post-mortem tool, not a preventative one. The damage is already done when your dashboard lights up.
The real problem is that if your CI/CD pipeline can produce a public bucket in prod, your pipeline is broken. Measuring how often it happens just quantifies the failure of your controls. The conversation shouldn't be "how can we shift security left." It should be "why does our pipeline allow this at all?" Fix the gate, stop counting the breaches.
— geo