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Securing Untrusted AI

Stop untrusted AI before it acts.

You didn't train it. You can't audit it. Starseer establishes trust in every model and agent you run: before it runs, while it runs, and at the gateway where your policy is enforced.

Request a demo Run the 4-minute diagnostic
Any model
Frontier, open-weight, fine-tuned, or self-hosted
~38ms
Policy enforced at the gateway, on every request
Air-gapped
Deploys with zero external dependencies

Where the gap is

You tested the model.

Benchmarks passed

Evals are green

Red team found nothing

Observability is wired up

None of it looked inside the model.

A trigger-conditioned backdoor scores exactly like a clean model, because the trigger was never in your test set. That gap did not matter much while every request went to a frontier API. It matters now.

Why now

01

Cost moves work off frontier APIs

Right-sizing spend cuts up to 85% of inference cost. That is too large a number to leave on the table.

02

Provenance becomes unknown

Open-weight models were trained by someone else, on data you cannot inspect, in some cases under another jurisdiction's requirements.

03

Untrusted becomes the default

Your network treats unknown traffic as untrusted. Your pipeline treats third-party code as untrusted. AI is the only layer still running on assumed trust.

The platform

Trust established at three points.

Most tooling stops at traffic and actions. Establishing trust means reading what the model was computing when it acted, and doing it at every point where you still have control.

Before it runs Scan A backdoored model passes every benchmark you run. Fingerprinting the model against an approved baseline catches what behavioral testing cannot. AI Model Vulnerability Scan → While it runs Oversee Agents act with your credentials. Reading activations and the decision-to-action chain contains a compromised session before the action lands. AI Detection & Response → At the gateway Enforce Every request classified before a model sees it, against guardrails your team wrote rather than refusals your vendor shipped. AI Gateway →

One measurement, taken while the model runs, serves all three.

See how the platform fits together

What that looks like

A case file, not just an alert.

When a probe fires on a model's internal state, the prompt, the tool call, and the full application trace are already on the same timeline.

Nothing to reconstruct. Nothing to correlate at 2am. Your analyst opens an investigation that is already assembled, and the session is already contained.

See how detection works →
case #4117 · agent session detection fired

prompt received

tool call · shell.exec

trace · app to gateway

probe · model activations

The probe fired on the model's internal state. Everything else was already on the timeline. That is the case file.

Proof

Deployed where trust could not be assumed.

Federal customer

Production in four weeks, fully offline, with no frontier API dependencies.

4 weeks
of a 16-week window
Air-gapped
zero external calls

SCYTHE · adversary emulation platform

“

From proof-of-concept to production in one month. Offline. No frontier API dependencies. We didn't think that was possible.

Jim Webster

Jim Webster

Director of Federal Programs, SCYTHE



Advised by

Rob Joyce

Former Director of Cybersecurity, NSA

Gary McGraw

Founder, Berryville Institute of Machine Learning

Contributing to

OWASP GenAI Security Project

MITRE ATLASDetections mapped to the framework

Find out what is inside the models you already run.

Four minutes, no call. The diagnostic scores your exposure across provenance, runtime, and policy control.

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