Technical Evidence for
Healthcare AI Risk Decisions
We help healthcare providers and healthtech companies evaluate AI assistants, RAG workflows, and sensitive data pathways before production deployment, audits, or enterprise reviews.
Why Healthcare AI Needs Independent Technical Evidence
Healthcare AI systems handle the most sensitive data possible: patient records, clinical decisions, and diagnostic workflows. A failure in these systems is not an inconvenience, it is a patient safety event.
Medical Directors, DPOs, CTOs, and boards need defensible technical evidence demonstrating that AI systems do not leak patient data, produce unsafe recommendations, or operate without proper audit trails, before going live.
Healthcare-Specific Risk Coverage
Focused assessment areas for healthcare and healthtech AI systems
AI System
Patient Data Exposure
AI systems revealing patient records, PHI, or sensitive clinical data to unauthorized users through response generation or RAG retrieval.
Incorrect Patient Context
Wrong patient data surfaced in AI responses due to retrieval boundary failures, context window leakage, or role-based access bypass.
Unsafe Clinical Recommendations
AI assistants providing guidance that could lead to patient harm through hallucination, outdated data, or missing clinical guardrails.
RAG & FHIR Access Boundaries
Retrieval-Augmented Generation accessing patient records beyond authorized scope, FHIR resource access without proper authorization checks.
Missing Audit Trails
AI decisions and data access events not logged, making incident investigation and regulatory evidence impossible to reconstruct.
Role-Based Access Control Failures
Clinical staff, administrative users, or external systems accessing AI capabilities or patient data beyond their designated role.
Cloud Logging & IAM Weaknesses
Cloud infrastructure misconfigurations allowing unauthorized access to AI models, patient data stores, or audit logs.
Patient Data Exposure
AI systems revealing patient records, PHI, or sensitive clinical data to unauthorized users through response generation or RAG retrieval.
Incorrect Patient Context
Wrong patient data surfaced in AI responses due to retrieval boundary failures, context window leakage, or role-based access bypass.
Unsafe Clinical Recommendations
AI assistants providing guidance that could lead to patient harm through hallucination, outdated data, or missing clinical guardrails.
RAG & FHIR Access Boundaries
Retrieval-Augmented Generation accessing patient records beyond authorized scope, FHIR resource access without proper authorization checks.
Missing Audit Trails
AI decisions and data access events not logged, making incident investigation and regulatory evidence impossible to reconstruct.
Role-Based Access Control Failures
Clinical staff, administrative users, or external systems accessing AI capabilities or patient data beyond their designated role.
Cloud Logging & IAM Weaknesses
Cloud infrastructure misconfigurations allowing unauthorized access to AI models, patient data stores, or audit logs.
Who This Is For
Healthcare Providers
Hospitals and clinics deploying AI clinical assistants or patient-facing tools
Healthtech SaaS
Companies building AI products for healthcare customers and enterprise sales
Digital Health Platforms
Platforms integrating AI for patient engagement, triage, or decision support
Life Sciences
Pharmaceutical and biotech companies using AI in clinical or operational workflows
Regulatory & Framework Alignment
Evidence mapped to the frameworks that matter to healthcare organisations
EU AI Act
High-risk AI system requirements for medical devices, clinical decision support, and patient-facing AI
GDPR & Health Data
Special category data processing, automated decision-making rights, and Data Protection Impact Assessments for AI
HIPAA
PHI safeguards, access controls, audit logging, and breach notification requirements for AI systems handling patient data
MDR / IVDR
EU Medical Device and In Vitro Diagnostic Regulation requirements when AI qualifies as a medical device
ISO 27001 & ISO 42001
Information security management and AI management system standards for healthcare technology providers
OWASP LLM Top 10
Prompt injection, sensitive information disclosure, and data leakage risks specific to healthcare LLM deployments
Ready to assess your healthcare AI risks?
Start with a 30-minute triage call to scope your assessment and understand your system-specific risks.
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Ready to understand your AI system risks? Let us help you generate the technical evidence you need for confident decision-making.
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Telbi provides technical evidence and remediation recommendations. We do not provide legal advice, conformity assessments, certifications, or guaranteed compliance.