Turn pre-release red teaming into a release gate and a training signal

Find real safety failures before launch. Gate every checkpoint. Convert findings into post-training data that fixes problems without killing capability.

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Safety teams

Pre-release evaluation

Post-training alignment

Checkpoint-level CI

Safer models that are also better models

Pre-release safety work should reduce real failures and preserve capability. Not one or the other.

Fewer trust-breaking incidents after launch

Adversarial coverage across your rubric catches the failures that matter — jailbreaks, harmful completions, refusal gaps — before users find them. Failures are pinned as regressions so they don't come back.

Higher adoption by consumers and enterprise deployers

A model with documented safety evidence — pass/fail by category, coverage maps, regression history — earns trust faster with enterprise buyers, platform partners, and regulators.

Faster, calmer releases across checkpoints

Rerunnable suites with diffs across checkpoints turn releases from ad-hoc fire drills into predictable gates. You can see exactly what got better, what got worse, and what's new.

Safety improvements without blunt over-refusal

Training signal is generated from your rubric — targeted SFT examples and preference pairs — so fixes address specific failure modes instead of making the model refuse everything.

From ad-hoc testing to a repeatable safety pipeline

Before Enkrypt AI

Safety work that doesn't compound

Manual red teaming that starts over every release

No structured coverage - you don't know what you haven't tested

Findings go into a report, not into the training pipeline

Over-refusal as the default safety lever

No way to diff safety across checkpoints

With Enkrypt AI

A pipeline that improves the model

Rerunnable suites pinned to your rubric

Coverage map: categories × languages × modalities

Findings become SFT examples + preference pairs

Targeted fixes that preserve capability

Severity-weighted deltas across every checkpoint

Three deliverables from every eval run

Each run against a checkpoint produces a release gate, a coverage map, and training data — not just a report.

Three steps to your first checkpoint eval

Connect, configure, run. Outputs include the gate pack, checkpoint diffs, and dataset exports.

1) Connect your checkpoint

Point to an endpoint, internal runtime, or hosted model — we evaluate wherever it runs.

2) Provide your rubric

Define policy categories, severity levels, and release thresholds that match your safety requirements.

3) Run and get results

Receive a Release Gate Pack, Coverage Map, and Training Signal — ready for your pipeline.

Multilingual, multimodal, adversarial

Testing spans the full attack surface — not just English text prompts.

Multilingual

Adversarial prompts across languages and locales, including low-resource language exploits

Multimodal

Cross-modal chains across text, vision, and audio - including prompt smuggling between modalities

Tool-use

If your model calls tools, coverage includes tool misuse, privilege escalation, and unsafe action sequences

Obfuscation

Encoding tricks, jailbreak chains, persona injection, and adversarial reformulation techniques

Built for model-builder security requirements

Your environment, your data

Runs in your VPC, on-prem, or enclave

Artifacts stored in your infrastructure

Configurable data retention policies

Access-controlled outputs with exportable audit trails

Strict access controls and private reporting

What we don't do

Not a public bug bounty or consumer reporting portal

Not a replacement for your internal eval stack — plugs into it

Not generic benchmark theater — tests are tied to your rubric

No public disclosure without explicit agreement

Frequently Asked Questions

Who is this for?

Safety and alignment teams at frontier labs who need pre-release adversarial evaluation, structured coverage reporting, and high-quality data for post-training safety tuning. If you're shipping foundation models, this is for you.

How is this different from standard red teaming?

Standard red teaming produces a report. Enkrypt AI produces a release gate (go/no-go with evidence), a coverage map (what was tested), and training signal (SFT + preference data) — all tied to your rubric and rerunnable across checkpoints.

How does the training signal avoid over-refusal?

Training data is generated from targeted failure modes — not blanket safety categories. SFT examples and preference pairs are aligned to your specific rubric, with a held-out regression set to validate that fixes don't degrade capability on adjacent tasks.

Does this support multilingual and multimodal evaluation?

Yes. Coverage includes multilingual adversarial prompts, obfuscation techniques, and cross-modal attack chains across text, vision, and audio where the model supports it.

Can this run inside our infrastructure?

Yes. Enkrypt AI can run in your VPC, on-prem, or in a secure enclave. All artifacts default to your storage, retention is configurable, and outputs are access-controlled with exportable audit trails.

How do we get started?

Define your target capabilities and risk categories, connect a checkpoint, and run your first eval. You'll get a Release Gate Pack, Coverage Map, and Training Signal from the first run.

Make safety measurable across checkpoints - and convert failures into performance-preserving fixes.