AI projects on GitHub
GitHub recorded rapid growth in repositories using LLM SDKs and AI-related projects.
GitHub Octoverse 2025 · 2025 ↗Updated 2026-10-04 · AI infrastructure
AI security
operations and reliability statistics.
A source-first view of change quality, recovery speed and whether the running system meets its objectives. 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 ↗malicious breaches were AI-enabled
IBM reported a 56% year-over-year increase in AI-enabled malicious breaches.
IBM 2026 Cost of a Data Breach · 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.
Change failure rate
Changes causing rollback, hotfix or incident ÷ all changes × 100
Median recovery time
Median time from impact detection to service restoration
Objective attainment
Measurement windows meeting the agreed SLO ÷ all windows × 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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