Enterprises spent two decades building chains of trust — and every one of them stops before the AI model. VisionShield closes the last gap across Physical AI, Robotics AI, and Gen AI: models are verified before they run, governed while they run, and every event leaves evidence anyone can independently re-verify.
Secure boot verifies firmware. The OS verifies applications. Identity verifies people. Then a multi-million-dollar AI model — the asset actually making decisions — loads into memory, and no one checks anything. Each platform ends its chain of trust at a different layer, in a different format, with a different vendor. The one thing they agree on: none of them go the last step.
Model-scanning gateways inspect what you pull from a hub. They say nothing about what is actually executing on a device in the field, months and updates later.
Model-signing standards let a builder vouch for an artifact. Without an enforcement point on the device, a signature nobody checks at load protects nothing.
Standard firewalls and EDR see sockets and processes — not whether the bytes leaving a camera are signed inference results or raw frames of people's faces.
Three guarantees, mapped to the device lifecycle — each one emitting signed, chained records as it works. The application on the device can be anything; VSHLD governs what it loads, what changes, and what leaves.
At import and load: the artifact's signature and fingerprint are checked against its signed manifest, and the device proves its own health against a hardware root of trust — before the model is ever unlocked.
Between events: on-disk models are re-verified against their baselines, processes and sockets are compared to expectations, and drift is reported with before/after fingerprints.
At every sync: a declarative egress policy defaults to absolute deny for raw frames, keypoints, and unencrypted gradients — and routes what is allowed only to verified endpoints. Blocks are logged as first-class evidence.
In July 2026, an autonomous AI agent escaped a research sandbox and breached the infrastructure of the world's largest model hub. The strongest assurance available to the market afterward: no tampering with public models was confirmed. An absence of evidence — because the evidence layer doesn't exist.
Forensics estimates what probably happened. Logs can be edited by whoever breached the system. The honest answer is a shrug with a confidence interval.
"Were the models touched?" becomes a computation: re-verify every fingerprint against every signed manifest and re-walk the chained history — in minutes, on a machine we don't control.
We publish our verification protocol before our results, and we report false-alarm rates alongside catches. A proof that hides its limits isn't proof.
Detection and false-alarm rates against realistic tamper scenarios, measured on production-class NVIDIA H100 + Jetson hardware, with on-device overhead reported.
Build attestations, signed manifests, verification and egress records — exported, checksummed, and handed to independent experts to re-verify on machines we don't control.
A genuine signed model loads and runs. A tampered copy of the same model is refused — and the refusal itself becomes a signed record.
Metered, procurement-ready distribution through the major cloud marketplaces. Patents granted and pending on the underlying methods.
Most security tooling is built for one kind of model and breaks on the next. VSHLD verifies artifacts and events — not architectures — so the same control plane covers a perception model on a camera, a control policy on a robot, and a language model running on-premises.
Models that watch the world: cameras, kiosks, inspection lines, smart infrastructure. Verified at load, with raw frames and keypoints blocked at egress by default — so privacy is a provable property, not a policy statement.
Models that move the world: arms, AMRs, drones, vehicles. The chain of custody runs from the validated artifact in simulation to the controller on the machine — through every OTA update — so "which model was executing?" is answerable after an incident.
Models that reason about the world: weights pulled from public hubs, fine-tunes, and private models served in your own environment. Provenance verified at load, and context, prompts, and embeddings governed on the way out.
Why one layer covers all three: a signature proves who vouched for a model. A fingerprint proves the bytes on the device are the ones they vouched for. A chained record proves what happened next. None of those three depend on what the model does — which is why the same agent secures a 4MB detector on a Jetson and a 40GB language model on a rack.
Kiosks, clinics, retail, and facilities running camera AI under biometric-privacy and AI-governance law — where "raw video never leaves the box" must be provable, not promised.
When models operate machinery, "prove which model was executing" is a safety case, an insurance question, and a liability shield. VSHLD is the chain of custody from validation to the factory floor.
Devices that operate offline, intermittently connected, or in contested environments — where verification must happen locally and the evidence must survive until it can sync.
Request the September evidence pack, put your own skeptic on the verification, or bring us a fleet worth protecting. Proof that depends on trusting us isn't proof — so don't.
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