Healthcare AI Security Audit
Medical AI reads the most sensitive data there is and informs decisions about patients. An audit tests whether models can be fooled, poisoned or made to leak records, and whether clinicians can still catch the error.
See the workflow →Four Areas to Test
A sector audit covers each of these areas.
- Data & consent
- Model robustness
- Clinical integration
- Monitoring & incidents
Core Concepts
The foundations of this sector's AI risk.
Why healthcare is different
Errors harm patients, and training data is personal health data under strict rules. Imaging, triage and documentation tools each have their own failure modes.
- Adversarial or corrupted inputs can flip an imaging or triage result
- Poisoned or mislabeled training data degrades a model silently
- Clinical LLMs can leak patient records or follow instructions hidden in documents
Regulatory frame
Medical AI sits at the intersection of device, data and AI rules.
- EU MDR/IVDR for AI that is a medical device, with IEC 62304 and ISO 14971
- GDPR (health data is a special category); in France, certified health-data hosting (HDS)
- EU AI Act: AI in regulated medical devices is high-risk, with obligations phased in over time
The Audit Workflow
Each phase is methodical and repeatable.
Scope the clinical use
Identify the intended use, the data flows, who acts on the output and what a wrong output costs the patient.
Test the model
Check robustness to noise, artifacts and adversarial inputs; look for poisoned or mislabeled data; run membership inference and extraction tests.
Test the integration
Probe clinical LLMs and RAG over patient records for leakage and injection; check access control, logging and de-identification.
Report and monitor
Link findings to the risk file and post-market monitoring; define drift triggers and an incident process.
Related Pages
Deep dives into complementary topics.
Model Extraction & Privacy Attacks
Model theft, membership inference.
AI Supply Chain Security
Datasets, models, dependencies.
AI Governance & EU AI Act
Obligations, controls, audit evidence.
AI Audit & Security Framework
Scope, method, evidence, report.