Is your healthcare AI safe to put in front of patients and PHI? AI SafeGuard™ is adversarial testing for the LLM apps, chatbots, agents, and RAG pipelines your teams are deploying right now.
100% healthcare-focused. Practitioner-led testing by certified AI penetration testers and healthcare security practitioners.
Healthcare organizations are putting generative AI into production faster than security teams can test it. Patient-facing chatbots, clinical copilots, RAG pipelines connected to PHI repositories, AI-powered APIs, and agentic workflows touching scheduling, billing, and clinical systems are live before anyone has probed them adversarially.
Traditional application testing was built for a different attack surface. AI systems introduce prompt injection, sensitive information disclosure, system prompt leakage, excessive agency, insecure tool and API use, RAG poisoning, insecure output handling, and AI supply chain risk. A standard CVSS score tells you the technical severity. It doesn’t tell you whether a successful exploit exposes PHI, corrupts a clinical workflow, or puts patient safety at risk. Your AI deployments carry healthcare-specific consequences, so they deserve healthcare-specific testing.
The in-scope AI systems, test windows, and access confirmed with application and environment owners.
Certified AI penetration testers exploit the systems from an attacker’s perspective, aligned to the OWASP Top 10 for Large Language Model Applications and the OWASP Machine Learning Security Top 10.
Every finding evaluated for its effect on PHI, clinical workflows, and patient communications, not just its CVSS score.
Testers who understand adversarial machine learning work alongside practitioners who understand PHI flows and clinical workflows, so testing reflects both how a model fails and what that failure costs in a care setting.
Findings map back to the AI governance and certification work that has to follow, so a test result becomes a control your team can operationalize, not just a ticket that gets closed and forgotten.
LLM chatbots and conversational AI, AI agents and autonomous workflows, RAG pipelines, AI-powered APIs and model endpoints, internal workforce copilots, and third-party AI integrations.
The AI Security Assessment evaluates design and risk. AI SafeGuard™ actively exploits the system from an adversarial perspective. The two complement each other, and many organizations use both.
AI system access, agreed test windows, and your application and environment owners available during testing.
Schedule a 30-minute conversation to walk through your current AI deployments and identify where adversarial testing reduces the most patient, PHI, and clinical risk.
