Secure the AI behind every patient interaction.
Triage chatbots, ambient scribes, clinical copilots, agents that read the EHR — healthcare AI handles PHI and shapes care decisions at the same time. SecuraAI discovers, tests, and governs it against the threats and regulations unique to medicine.
In healthcare, an AI failure is a patient-safety event.
AI has moved from the back office to the bedside. Intake and triage chatbots talk to patients directly. Ambient scribes capture the visit. Copilots draft notes and prior-authorization letters. Increasingly, agents query the EHR, schedule, and act — each one handling protected health information and influencing a clinical decision.
That changes the risk calculus. A jailbroken triage bot that gives unsafe advice, a copilot that hallucinates a dosage, an agent steered by an injected instruction to export records — these are not reputational dings. They are HIPAA breaches and patient harm, and they arrive with HHS/OCR, the FDA, state regulators, and plaintiffs close behind.
Securing healthcare AI means proving — continuously, and with evidence — that every model, chatbot, and agent behaves safely under pressure and keeps PHI where it belongs.
Where healthcare AI breaks.
The failure modes that matter most when AI meets patients and PHI.
PHI leakage & exfiltration
Agents and RAG systems that read patient records can be steered — by injection or over-broad access — into disclosing PHI to the wrong person or system.
Unsafe clinical guidance
Hallucinated dosages, missed contraindications, or confident-but-wrong triage advice turn a helpful chatbot into a safety risk.
Prompt injection via patient content
Messages, uploaded documents, and intake forms are attacker-controllable inputs an agent may treat as instructions.
Shadow clinical AI
Staff pasting PHI into unsanctioned chatbots is often the largest — and least visible — exposure in a health system.
Over-privileged agents
EHR, scheduling, and billing agents granted standing, broad access carry a blast radius far larger than any single task needs.
Inequitable outputs
Models that perform unevenly across patient populations create clinical and regulatory risk that surface testing won't catch.
How SecuraAI secures healthcare AI.
Inventory every clinical and administrative AI system — including shadow AI — and risk-tier each by PHI exposure and clinical impact.
- Find sanctioned and shadow AI across the system
- Risk-tier by PHI and patient-safety impact
- Continuous governance as new tools appear
Multi-turn adversarial safety testing for triage and intake chatbots and voice agents, with specialized clinical domains — built to catch unsafe guidance, not just policy violations.
- Probe for hallucinated or unsafe clinical advice
- Jailbreak and role-play resistance under pressure
- Specialized clinical-domain test suites
Probe EHR-connected agents and copilots the way an adversary would — prompt injection, PHI exfiltration, tool abuse, and goal hijack across multi-agent workflows.
- Direct & indirect prompt injection
- PHI exfiltration and tool-abuse paths
- Blast-radius testing for connected agents
Statically scan ML model artifacts — including diagnostic and predictive models — for unsafe serialization, malicious loaders, and supply-chain risk before they reach production.
- Detect unsafe serialization & loaders
- Surface supply-chain risk in model files
- Gate models before clinical deployment
Mapped to the rules healthcare answers to.
Findings structured as audit-ready evidence for the frameworks and regulators that govern medical AI.
Prove your clinical AI is safe — before it sees a patient.
Start with a free risk assessment. We'll probe a live system and show you exactly where PHI and patient safety are exposed.