Usually not directly. An industry average time to patch describes a group, and the spread inside that group is often wide. Use it as rough context, then judge yourself against your own asset criticality, exposure and trend over time rather than against a headline figure.
Look at the spread, not the average
In Tenable's analysis of 17 edge device CVEs, the sector averages for a single flaw could differ widely. For one management product flaw, a communications sector averaged 12 days and healthcare averaged 71 days. For another pair of flaws, even the fastest sector took more than 260 days (Tenable). The same report author noted that the lowest averages for some flaws were much higher than expected.
Two lessons follow. The variation between flaws is as large as the variation between sectors. And a sector label says little about a specific company's practice.
Reasons your situation differs
| Factor | Why it changes a fair target |
|---|---|
| Exposure | An internet facing system deserves a shorter clock than an isolated one |
| Asset criticality | A payment system and a test box should not share one deadline |
| Change process | Regulated change control adds days that are not negligence |
| Fix type | Config change, package update and vendor firmware differ |
| Flaw type | Known exploited flaws deserve less waiting than theoretical ones |
A better way to use a benchmark
- Treat the published number as an order of magnitude check. Are you in the same range of days, or an order different?
- Split your own data by exposure and by known exploited status.
- Set targets for each tier. The CISA known exploited catalog is a reasonable input for the top tier, because it lists flaws with evidence of exploitation (CISA).
- Track your own trend each quarter. Improvement against yourself is more informative than distance from an average.
What leaders actually want to know
Executives rarely need a sector comparison. They want to know whether the riskiest assets are fixed fast and whether the slowest items have an owner. Answer that, and use the external figure as a footnote.
A worked example
Suppose your median time to fix a critical internet facing flaw is 20 days and a report you read says sectors averaged well over 100 days for a set of exploited edge flaws. It is tempting to relax. But your median covers fixes you completed. Check the slowest quarter of your own items and the ones tied to known exploitation first. If those are also fast, you are in good shape. If the slow tail holds your exposed assets, the comparison flattered you.
The reverse case matters too. If your numbers look worse than a published figure, check whether your clock starts earlier than theirs. Starting the clock at first detection will always look slower than starting at patch release.
Safeguard can help with the inventory side of this. Its SBOM and dependency scanning and reachability analysis show which vulnerable components you run and which your code actually calls. You can try the free scan at gold.safeguard.sh/scan.
Quick check
Should my SLA match the industry average?
No. Base it on exposure, criticality and exploitation evidence, and use industry figures only as context.
Is being faster than average good enough?
Not by itself. An average can be high, and attackers act on their own timeline, not the sector's.
Sources
- Tenable, Verizon 2025 DBIR research collaboration on CVE remediation trends
- CISA, Known Exploited Vulnerabilities Catalog
Last reviewed: 19 October 2026.
