For the DevSecAIOps engineer
Your old gates catch bad code. Upgrade your pipeline for AI.
Track 2 · Your skills path is one click away. The story below is depth; the path names what to learn.
Score your mastery →Injection, tool misuse, a silent model swap — none of them are code defects.
They walk straight through CI.
53% → 100%
a shipped guard caught just over half of its own attack classes; fitted to them, all of them — held-out set included. The scorer that produced those numbers is the same check you’d wire into the pipeline.
the fitted-guard case study — modeled on a fixed corpus, every number labeled
Your existing gates are fine at what they were built for. AI artifacts fail differently: the defect is in behavior, not syntax.
So the fix is more gates, not better linting — eight of them, mapped to stages you already run.
“Which gates do I add — and how do I test them like everything else I ship?”
Eight gates, mapped to stages you already have. One of them proven end to end, with the numbers.
Three beats: the gates, what each one checks, and the one that’s already wired.
The gates
G0 to G7, across the whole lifecycle.
Design through Attest, one gate per stage — each crossed by three lanes: classic DevSecOps, AI-augmented work, and AI artifacts themselves. The last gate feeds evidence back into the first.
Nothing exotic: they sit on the CI stages you already run.
G0 Design: before any code or model work begins — Risk class, the AI-vs-human allocation decision, a harm/misuse assessment, and the DPIA decision.
The eight gates on your lifecycle, walked one by one — Design through Attest, then back around. Free to read in full; start with the fitted-guard proof below.
CI-shaped
Deterministic input, binary verdict.
Every gate check is built like a unit test: fixed input, pass/fail output, no vibes. Evals are to AI what unit tests are to code — and each eval suite blocks independently, because a model can pass security red-teaming and still fail its fairness threshold.
If a check can’t be made deterministic, it isn’t a gate yet — it’s a review.
The worked example
One gate, proven end to end.
Gate #4 — prompt-injection red-team as a CI gate — has a full worked example: a real app fitted to its own attack classes, the before/after on a fixed corpus, and the scorer wired in as the release check.
Modeled, not measured — and every number says so.
Your reskilling list
21 controls have your name on them.
Owned where the pipeline is the control. Each group lands on gates you already run.
1 to build · 8 to coordinate with a provider · 12 to verify, not build. Nobody reskills for what the provider already owns.
Gate the pipeline
6 controls
Guard the interfaces
5 controls
Manage change and config
8 controls
Ship with provenance
2 controls
This is the same spine the assessment reads. Score your mastery on four concrete rungs per prompt, or run the function diagnostic — every gap lands on this list: the named skill, the group it belongs to, and who learns it.
Browse skills personalities →Run the diagnostic. Your gaps land on this list →
53% → 100% catch — then wired in as gate #4.
Modeled on a fixed corpus, held-out included — the scorer is the gate you ship.
Not your role?
Each role has its own way in. Here is where the others start.