AI Security & Red Teaming for Healthcare

Healthcare AI sits on top of PHI, and HIPAA makes any unintended disclosure a reportable event, not an inconvenience. A clinical assistant or prior-authorization tool reads charts, notes, and payer documents, which means untrusted content flows straight into the model and PHI flows back out. Prompt injection can turn a patient-uploaded form into an exfiltration vector, and a jailbreak can push an assistant past its human-in-the-loop guardrails into unsafe clinical territory. We adversarially test the AI you embed in EHR and prior-auth workflows, harden it against PHI leakage and unsafe outputs, and keep the human checkpoint enforceable rather than optional.

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How we deliver it

AI Security & Red Teaming, built for healthcare

We threat-model around PHI flow and clinical safety: where untrusted notes, forms, and payer documents enter, and where protected data or unsafe advice could exit.

We run injection and exfiltration tests against your EHR-integrated assistant, proving whether a crafted note or uploaded document can leak PHI or trigger unauthorized actions.

We attack the human-in-the-loop boundary, verifying the model cannot be jailbroken into bypassing clinician review or asserting unverified clinical conclusions.

We harden with output filters, minimum-necessary access, and audit logging that supports HIPAA accounting-of-disclosures and your security risk analysis.

Where it pays off in healthcare

PHI exfiltration testing

We attempt to extract protected health information through crafted prompts, poisoned notes, and tool outputs, then close every path that leaks.

Prior-auth manipulation

We probe whether payer or clinical documents can inject instructions that alter authorization logic or surface another patient's data.

Human-in-the-loop enforcement

We test that no jailbreak lets the assistant finalize clinical decisions without the required clinician checkpoint.

EHR boundary hardening

We verify the model honors minimum-necessary access so it cannot pull records beyond the patient and purpose at hand.

Healthcare clients close out PHI leakage and unsafe-output paths before go-live and keep the clinician checkpoint enforceable, reducing HIPAA exposure on every AI-assisted encounter.

Healthcare AI, answered

We work against staging with synthetic patient data wherever possible, and any production testing runs under a BAA with scoped, logged access. The goal is to find leakage paths in a controlled setting before a real attacker or accident does.

That is exactly what we test. We attempt jailbreaks aimed at getting the model to bypass clinician review or assert unverified conclusions, and where we succeed we harden the boundary so the human checkpoint cannot be skipped.

Yes. Our threat model, findings, and remediations feed directly into your required security risk analysis, and the audit logging we add supports accounting of disclosures. You get documentation that shows the AI was tested and controlled.