Solution · AI in the SDLC

Ship AI like you ship code.
Gate every model change.

You already gate deploys on tests and coverage. Klyvra adds one more gate: an adversarial red-team scan that runs on every model change and blocks the release when the model is not safe enough. It plugs into the pipeline you already run - GitHub Actions, GitLab CI, or a plain script - and hands the pipeline one thing back: pass or fail.

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Solution · AI in the SDLC

Shift AI security all the way left.

A red-team scan at release time is only useful if it can actually stop a release. Klyvra makes that the default. Your deploy pipeline calls Klyvra to scan the changed model, then polls for a verdict: score at or above your threshold returns pass and the deploy proceeds; below it returns fail and the deploy is blocked. It is the same deep adversarial evaluation Klyvra runs everywhere else - the same suites, the same Lelouch AI judge - just triggered by your pipeline instead of a person, and attributed back to the pipeline that fired it.

The deploy gate

One call in your pipeline. A pass or fail back.

The gate is a trigger, a poll, and an exit code - the same contract you already trust from your code scanners. Six things make it safe to put in the critical path of a production deploy.

Gate deploys on a real scan.

Every model change is tested against 40+ adversarial suites by the Lelouch engine before it ships - not sampled after. A model that fails the bar you set never reaches production on a green pipeline.

A verdict built for a pipeline.

Your CI job gets back one slim pass or fail and gates on an exit code. No parsing a giant report to make a ship decision. When a developer wants the detail, the full red-team report for that same run is one authenticated call away.

Native GitHub Actions and GitLab CI.

The console generates a ready-to-paste job for you - a real GitHub Actions workflow, a real GitLab CI job, plus portable Bash and Python. Each is prefilled with your pipeline and suite, and reads your key from the CI secret store, so a credential never lands in your repo.

Fail-closed by default.

If a scan errors, times out, or cannot render a real verdict, the deploy is blocked - unless you explicitly opt into fail-open. Safety is the default posture, not a setting someone has to remember to turn on.

Your bar, your rules.

Set the score a model must clear to pass, per pipeline. Manage pipelines and their scoped, revocable API keys from the console - each key can run only the suites it is allowed to, and one click revokes it.

No orphaned scans, no runaway cost.

A backend watchdog cancels any scan that runs past your deadline, so a dead CI runner never leaves a scan burning GPU. Pipeline scans are also exempt from manual rate limits, so a release gate always fires on every deploy.

Who it's for

Built for the teams who own the deploy pipeline.

AI-in-the-SDLC gating is for the platform, AppSec, and ML-engineering teams who own the path from a model change to production. If a fine-tune, a new system prompt, or a swapped foundation model can reach customers through a pipeline, that pipeline is where a security regression should be caught - before the release, not in next quarter's incident review.

What this unlocks

Outcomes you can defend in a review.

Outcomes Klyvra customers and design partners use to justify the programme to their boards, auditors, and clients.

Catch regressions before they ship.

A prompt change or fine-tune that reopens a closed vulnerability category fails the gate on the same deploy that introduced it - not weeks later on a scheduled scan.

Make the safe path the default path.

Once the gate is in the pipeline, shipping a model that has not passed a red-team scan stops being possible by accident. Security review becomes a daily signal, not an annual event.

One secure front door for machines.

Every pipeline call goes through a single hardened, TLS-terminated API gateway. Your network team allowlists one hostname; nothing else in the platform is ever exposed.

Sovereign and zero-egress.

The scan, the verdict, and the report all stay inside your cluster - the same hard requirement that governs the rest of Klyvra. Your model and its responses never leave your network.

One platform.
Every way to deploy it.

AI Posture Management

Put a safety gate
in your pipeline.

Bring a pipeline and a model you ship through it. We will wire a Klyvra gate into a GitHub Actions or GitLab CI job during a 30-minute walkthrough and show you a real deploy pass and fail end to end.

Request a demo →[email protected]

Ship AI like you ship code.Gate every model change.