reported adopting cloud-native techniques
CNCF described cloud-native adoption as near-universal among surveyed organizations.
CNCF Annual Cloud Native Survey · 2026 ↗Updated 2026-10-04 · Cloud native
Kubernetes
security risk statistics.
A source-first view of exposure, attack paths and the speed at which material findings are closed. Published findings provide context; the measurement framework shows what to calculate inside your own environment.
01 / Published evidence
What the public data says.
Each figure links directly to the organization that published it. Vendor telemetry and community surveys are labelled rather than blended into one invented industry average.
of generative-AI hosts used Kubernetes for inference
CNCF found Kubernetes managing some or all inference workloads for two-thirds of this group.
CNCF Annual Cloud Native Survey · 2026 ↗reported profiling in the observability stack
The CNCF annual survey highlighted profiling as an emerging observability practice.
CNCF Annual Cloud Native Survey · 2026 ↗02 / Your baseline
Measure the system you actually run.
Use these formulas consistently for at least one complete operating cycle. Keep scope, exclusions and collection dates beside the result.
Critical exposure density
Open critical exposures ÷ 100 in-scope assets
Median remediation age
Median days from validated finding to verified closure
Repeat finding rate
Reopened or recurring findings ÷ all closed findings × 100
03 / Interpretation
A number without a denominator is decoration.
Segment results by environment, workload owner and business criticality. A falling average can hide one critical service getting worse; a rising count can simply reflect better discovery.
- Record the source system and collection window.
- Separate discovered, in-scope and actively managed populations.
- Keep medians and tail values where averages hide risk.
- Annotate architecture, policy and tooling changes.
- Recalculate after remediation instead of closing on intent.
Related benchmarks
Look at the adjacent signal.
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