SichGate for Healthcare & Clinical AI
Your patients shouldn't be your first red team.
Clinical language models fail differently than typical software fails. A wrong answer isn't a bug ticket. It's a patient safety incident.
SichGate runs independent, adversarial testing on healthcare and clinical AI models before they reach a patient, and produces verifiable evidence, not another vendor claim.
[ 01 ]
TARGET
PROFILES
Technical teams shipping a fine-tuned or quantized language model into a healthcare workflow. Clinical documentation, triage support, patient-facing chat — anything where a bad output has a real consequence, not just a support ticket.
[ 02 ]
SYSTEMIC
CRITICALITY
Most healthcare AI safety claims rest on the vendor's own word.
A model card. A benchmark score. A line in a sales deck.
Benchmark scores describe average case behavior. They don't describe what happens when someone deliberately tries to break the model. In a clinical setting, that gap is where the damage happens.
Two things make it worse in healthcare specifically:
Module 02.1
Fine-tuned and quantized models drift.
A model that passed a safety review in its base form can behave differently once it’s fine-tuned on a hospital’s data or compressed to run in production. Few teams re-test after that transformation.
Module 02.2
The failure modes aren’t generic.
A model that resists a jailbreak on a general purpose benchmark can still expose patient data, hallucinate a clinical recommendation, or fail to escalate when it should. Healthcare-agnostic testing won’t catch a healthcare-specific failure.
Without independent, adversarial evidence, "the model is safe" is a claim. Not a fact.
[ 03 ]
TELEMETRY
OUTPUTS
Adversarial probe batteries, across the full lifecycle.
SichGate runs adversarial probe batteries against a model across its full lifecycle: base, fine-tuned, and quantized. The core battery currently spans 179 probes across 25 attack techniques, benchmarked against published frameworks including AdvBench, HarmBench, and JailbreakBench.
Testing runs before deployment, as a release gate, and can run again after deployment to catch drift as the model or its data changes.
The output isn't a score on a landing page. It's evidence, built to sit in front of security, compliance, legal, and the board — without needing SichGate in the room to explain it:
Signed assessment report
ed25519-signed, tied to the exact test run.
Weight hash
A sha256 hash of the exact model weights tested, so the report can’t quietly drift from what’s actually in production.
AI-BOM
A CycloneDX 1.6 AI-BOM, in a format security and compliance teams already know how to read.
[ 04 ]
TIERING
PROTOCOLS
A tier tells a reviewer, at a glance, what the testing actually covered.
Models that pass testing receive a certification tier: SG-1 through SG-4 for testing with access to the weights and quantization path, or SG-S for API-only or behavioral screening when the weights aren't accessible.
Full weight and quantization-path access, hardened across the battery.
Full weight and quantization-path access, cleared with limited findings.
Full weight and quantization-path access, deployable with documented mitigations.
Full weight and quantization-path access, unresolved critical findings.
API-only or behavioral screening, for when the weights aren’t accessible.
[ 05 ]
REGULATORY
MAPPING
Two different things. On purpose.
SichGate's adversarial testing and compliance framework mapping — HIPAA included — are two different things. The adversarial testing runs against the model itself. Compliance framework mapping is a separate assessment, delivered by SichGate's team.
Protocol: independence
It's never bundled into the automated output, and it's never sold as native or automatic, because it isn't. Keeping the two apart is what keeps both of them honest about what they actually verified.
[ 06 ]
ENGAGEMENT
MODES
Mode 01 · Self-serve
Platform
Run the same versioned adversarial battery against your model, self-serve, any time you want to test a build. Every model gets tested against the same 179 probes across 25 attack techniques. That consistency — tested the same way, every time — is what makes the evidence comparable across models and over time.
START FREE ASSESSMENT →Mode 02 · Healthcare & regulated deployments
Contract
For healthcare and other regulated deployments, SichGate's team also works directly with clients under contract: custom attack test cases, remediation, and where it's needed, a custom air-gapped deployment built for that environment. This is a separate, disclosed engagement, scoped to what the client actually needs, not part of the automated platform output.
DISCUSS A CONTRACT ENGAGEMENT →External validation
A 2026 peer-reviewed study in Nature Medicine reached a similar conclusion from the outside:
“Benchmark performance doesn't predict how frontier models hold up under adversarial pressure in health AI applications.”
Self-reported safety numbers, without independent verification, are increasingly the norm across the industry.
That's the gap SichGate exists to close.
Nature Medicine
2026 peer-reviewed study
[ 08 ]
SYSTEM
CLOSING
Every clinical model is different, and so is every organization's risk profile and data. The battery above is the general-purpose core. For healthcare engagements, SichGate tailors the attack battery to the specific model, its use case, and the data it touches.
TALK TO SICHGATE ABOUT YOUR MODEL →The technical detail on healthcare-specific probe categories gets built out with each engagement, not shipped as one page fits all.