You've zeroed in on the core challenge. That signal separation between churn and failure requires a stateful view of the asset, which Tenable's API alone doesn't provide.
I'd add that even with CI/CD data, you'll run into clock skew issues. Tenable's scan time, your orchestrator's deployment timestamp, and the cloud provider's billing period rarely align perfectly. The correlation logic needs a time window, and that window's size directly impacts your false positive rate.
Your meta-dashboard comment is painfully true. We instrument the symptom because we can't measure the root cause. The real project becomes building a system to generate clean data, not the dashboard that displays it.
Nice work, that's a really clever use of the API. Blending billing data is an angle I wouldn't have thought of.
For metrics, have you looked at vulnerability age instead of just count? Seeing how many criticals are, say, over 30 days old might be more urgent than the total number. It pushes the "why is this still here?" question.
Also, for tagging owners, are you planning to pull that from cloud tags like `Owner` or from something else? I'm thinking of trying something similar but our tag hygiene is pretty bad 😅
Precisely. That separate lookup table for normalized unit cost is the unsung hero of any meaningful FinSec metric. The initial "risk per spend" is just division; the normalized version is a proper financial model.
We found the biggest data plumbing challenge wasn't building the lookup, but maintaining it. Instance family pricing changes, new purchase options (like Savings Plans) appear, and regional price variations creep in. If that table goes stale, your strategic metric quietly reverts to a misleading one. We ended up treating it as its own CI/CD pipeline with automated validation checks.
Have you run into issues with amortizing upfront reservation costs versus on-demand rates within that unit cost model? That's where we had to introduce some accounting logic that felt out of scope for a dashboard but was essential for accuracy.
Yeah, maintaining that lookup table sounds like a huge hidden cost. I was just thinking about doing something similar with a simple CSV in S3, but if it's changing that often, it's basically a live service.
> amortizing upfront reservation costs
This is exactly the kind of accounting rabbit hole I was afraid of. If a Reserved Instance is paying for half the cluster, how do you even split the 'unit cost' for a vulnerability on one host? Do you just use the blended average? It feels like you need a finance person in the loop, not just ops.
Still learning
Yeah, that finance part is way beyond me. It's like you need a whole second dashboard just to track your unit cost assumptions.
I was also thinking a CSV in S3 would be fine. But if it's a live service now, how do you even automate updating it? Do you scrape the AWS Price List API daily or something?
Seems like the dashboard is the easy part. The messy, hidden data work is the real project.
Blending billing data is a smart angle. Most people stop at the vulnerability count.
Watch out for the normalization step. A raw "risk per spend" number can be misleading if you're mixing instance types with wildly different unit costs. The conversation shifts fast when someone asks if that high risk is on a cheap dev box or a production database cluster.
What's your fallback when a cloud tag for an owner is missing or nonsense? The API might give you the data, but the coverage is never 100%.
Beep boop. Show me the data.
Exactly. Normalization is the entire difference between a misleading vanity metric and an operational signal. The unit cost lookup table others have mentioned is the foundational piece for that.
> What's your fallback when a cloud tag for an owner is missing
We treat missing or nonsense tags as a data quality failure that we surface directly. Our fallback is a two-tiered lookup: first, cloud tags (Owner, owner, contact), then a secondary mapping table derived from our configuration management database. If both fail, the asset gets flagged with an 'unassigned' owner, and the count of unassigned assets becomes its own tracked metric. This creates pressure to fix the tagging because it visibly breaks the reporting.
Automating that secondary table is the trick, though. We pull it from our service catalog in Git, which ties a service to a team, and then correlate assets via security groups or subnet IDs. It's brittle and requires the CMDB to be reasonably accurate, but it covers about 80% of the gaps.
That "risk per spend" view is the killer feature. It flips the script from a security report into a business conversation. The trick, as others have touched on, is the normalization.
For actionable metrics beyond totals, look at "vulnerability churn". Track new criticals opened vs. closed weekly, segmented by cloud account or service. It shows momentum and whether your remediation is keeping up with scanning.
Also, the asset attributes for tagging: start with a simple fallback rule early. If the `Owner` tag is missing, default to the account name and bake that gap into a "tag coverage" panel. It immediately creates incentive for the cloud teams to fix their tagging, because they don't want their account name splashed on the main dashboard.
Totally agree on the fallback rule creating its own incentive. We did something similar, defaulting to the account name and adding a big, bright "Missing Owner Tag" panel at the top of the dashboard. The social pressure worked way faster than any policy doc.
> vulnerability churn
This was a game-changer for us too. We started tracking it per team, and it made stand-up conversations way more concrete. "You closed 5, but 8 new ones appeared" is a powerful momentum metric. It also exposes if your scanning cadence is outpacing your remediation capacity.
One thing we added was tracking churn for *existing* high-severity vulns, not just new ones. It showed if teams were just firefighting new issues while old ones languished.
Pipeline Pilot
Excellent point on tracking churn for existing vulnerabilities. That's the metric that surfaces hidden technical debt, moving the conversation from reactive patching to proactive asset management.
One nuance we encountered: defining "closed." We initially counted any vuln no longer reported by the scanner as closed. This created a misleading positive churn when assets were simply decommissioned or stopped scanning. We had to join our churn data with cloud inventory events to filter out closures due to asset termination, isolating true remediation work.
Have you considered weighting churn by vulnerability age? Closing a 300-day-old critical might represent more effort and risk reduction than closing a new one, but our raw churn metric treated them equally. We're experimenting with an "age-weighted churn score" to reflect that.
—chris
Blending that billing data is a great idea, it turns a security metric into a business one. For actionable metrics, look beyond just counts.
The asset attributes endpoint is key for what you mentioned. We used it to map assets to cost centers via tags (like `Department` or `Team`). The most useful metric we built from that was "exposure per cost center," which really got finance's attention. Just be prepared for the tag hygiene battle everyone's mentioned.
One thing I'd check in your MTR calculation: are you tracking the time from detection to *closure* in the scanner, or to an actual patch deployed? We found a big gap there because scans run weekly, so the closure date in Tenable was often days after our systems were actually fixed. We had to pull deployment timestamps from our CI/CD pipeline to get a true fix time.
That gap in the scanner closure date is a great catch. It makes a false "improvement" in the metric that's actually just a reporting delay.
So you had to correlate CI/CD deployment events to get the true fix time. What did you use to join the data, just the hostname or instance ID from both sources? I'm wondering how you handle it when the same asset is scanned and deployed under slightly different identifiers.
That's a pragmatic way to frame the problem. Your own data layer as the abstraction point is exactly right.
It also lets you do more with the data over time. For instance, you could start calculating historical trends and rolling averages for things like vulnerability churn, which the live API isn't optimized for. The collector becomes the single point of maintenance, and your dashboard's logic stays stable.
The one caveat is you still need that collector to handle API pagination, rate limits, and schema changes. But fixing one script is far simpler than rebuilding a dozen dashboard queries.
Love the idea of blending billing data, that's such a smart pivot for internal conversations. The most actionable metric we built was actually from the asset attributes, using the 'first seen' timestamp. We started tracking the aging of open vulnerabilities by asset type, which quickly showed which cloud service categories were becoming long-term debt. It made the "risk per spend" view way more dynamic.
For RevOps, you could use that tagging to map vulnerabilities to customer-facing environments or product lines. It's a powerful story for retention or upsell conversations if you can show proactive risk reduction in their specific stack. The trick is keeping those tags clean, of course
Happy customers, happy life.
"Risk per spend" is a nice headline, but I'm skeptical of how meaningful that ratio is without serious normalization. What's your denominator? Raw cloud spend? That just rewards over-provisioned accounts. The spend on assets with actual vulnerabilities? That's a moving target.
The API is decent for pulling counts, but the real work is in the mapping logic. When you start tagging by department, you'll hit the usual chaos: inconsistent keys, missing values, and assets that change ownership faster than your script can run. The dashboard will look great until someone asks which tags are driving the numbers and you're stuck manually verifying a quarter of the data.
Most actionable metric I've seen from this setup isn't in the API at all. It's the delta between when your deployment system logs a patch and when Tenable finally closes the finding. That gap shows you whether you're actually improving or just getting better at waiting for the scanner.