Your AI is now part of your attack surface. Assess it before an auditor or an adversary does.
Healthcare organizations are deploying AI into clinical documentation, prior authorization, imaging, patient triage, and revenue cycle faster than security and governance programs can keep up. These systems learn from PHI, make decisions inside clinical workflows, and create risks traditional security reviews were never built to catch.
Training data can carry integrity and bias problems that surface as patient-safety and equity exposure. Models can be manipulated through adversarial inputs. Deployment environments stitch together cloud services, third-party APIs, and vendor models, each widening the attack surface. Most healthcare security teams are lean, and AI risk sits in the gap between the data science teams building models and the GRC teams accountable for compliance, so AI moves into production without a clear picture of where it can fail or what to remediate first.
Every finding is interpreted through a healthcare lens: PHI exposure, clinical workflows, and regulatory expectations, not just technical severity.
How the system can be reached, influenced, and abused across its data, model, and integration layers.
In healthcare these become patient-safety and equity issues, not just technical findings.
The cloud services, APIs, and access paths around the model, not just the model itself.
How the system holds up against the prompts and inputs designed to make it behave in unintended ways.
Against leading guidance such as NIST AI RMF, ISO/IEC 42001, HITRUST AI, and the OWASP ML Security Top 10, with findings prioritized, not just listed.
This assessment evaluates design and risk. AI SafeGuard™ actively exploits the system. Many healthcare organizations run both.
Security testing built for traditional applications does not find what breaks an AI system. Prompt injection, training-data integrity, model endpoint exposure, and RAG pipeline leakage are a different class of finding, and most healthcare security firms aren’t yet equipped to look for them.
A bias finding in a triage model is a patient-safety issue. A prompt injection path into an ambient documentation tool is a PHI disclosure path. We assess AI in that context, not in the abstract, and findings arrive mapped to the frameworks your certifications already use.
This evaluates design and risk: the threat model, data integrity, bias, adversarial robustness, and control gaps. AI SafeGuard™ actively exploits the system. The two are complementary, and many organizations run both.
Yes. In a healthcare setting, bias and data-integrity risks carry patient-safety and equity implications, so we treat them as core findings, not an add-on.
NIST AI RMF, ISO/IEC 42001, HITRUST AI, and the OWASP Machine Learning Security Top 10.
Schedule a 30-minute conversation to walk through your current AI footprint and identify where an assessment reduces the most risk.
