VIGIL — Clinical AI Audit
Audit AI-patient conversations and clinical SOAP notes for hallucinations, safety violations, and compliance issues. Processed under signed BAAs. Zero data retention. Built on HIPAA-compliant infrastructure.
HIPAA-Compliant Processing
- All data processed under a signed HIPAA Business Associate Agreement on HIPAA BAA-covered cloud infrastructure
- Zero data retention — content held in memory during audit only, never stored or logged
- Encrypted in transit via TLS between your browser and audit infrastructure
- No patient data is used for model training
Multi-Model Consensus
Independent verification across AI systems
Practicing Physician-Scientist
Practicing cardiologist (FACP, FACC), 150+ publications, ML training at Georgia Tech
Comprehensive Safety Flags
13 clinically specific detection categories
Real-Time Streaming
Watch audit progress in real time
Audit Capabilities
Every submission goes through rapid screening, deep clinical analysis, and independent multi-model cross-verification.
AI Conversation Audit
Audit AI-patient conversation transcripts for prompt injection, clinical hallucinations, fabricated medical authorities, scope violations, and safety threats. Every conversation is screened, analyzed, and cross-validated by multiple independent AI systems.
Try it →Clinical Note Audit
Audit SOAP notes and clinical documentation generated by AI systems for dosage safety errors, documentation integrity failures, transcript infidelity, security violations, and compliance issues. Automated SOAP note audit catches errors before they enter the medical record.
Try it →Enterprise Batch ProcessingEnterprise
Upload CSV files with hundreds of records for automated batch auditing of AI-patient conversations and clinical notes. REST API integration for CI/CD pipelines, structured JSON results, and audit trail documentation.
Learn more →How It Works
Submit
Upload a conversation transcript, clinical note, or CSV batch through the web interface or REST API.
Automated Audit
The system runs your content through a proprietary multi-stage pipeline. Multiple independent AI systems cross-validate findings so no single model determines the result.
Actionable Results
Receive a structured report with flagged issues, severity levels, confidence scores, and recommendations — streamed in real time or delivered as a batch report.
Data Security & HIPAA Compliance
Enterprise-grade security infrastructure designed for protected health information, with signed BAAs and zero data retention at every layer.
Data Handling
- Zero data retention — content held in memory only during audit
- No patient data is logged, cached, or persisted to disk
- No patient data is used for model training
Infrastructure
- HIPAA BAA-covered cloud infrastructure
- Direct browser-to-server CORS — no intermediate data storage
- TLS encryption in transit, WAF protection
Compliance Framework
- HIPAA Administrative, Physical, and Technical safeguards
- Sub-Business Associate Agreement with cloud infrastructure provider
- Hosted on cloud infrastructure whose provider holds a SOC 2 Type II report
Enterprise ControlsEnterprise
- SSO integration and role-based access control
- Audit trail documentation for compliance reporting
- Custom data handling agreements available
Built for Your Organization
Purpose-built healthcare AI quality assurance for the organizations that need it most.
For Digital Health Companies
Integrate audit into your CI/CD pipeline via REST API to catch hallucinations, safety violations, and scope issues before patients encounter them. Structured audit reports you can include in your own verification and validation documentation.
- Pre-deployment validation via REST API
- CI/CD integration with structured JSON results
- Catch the 27% hallucination rate in healthcare AI before release
For Health Systems and Hospitals
Validate vendor AI chatbots and documentation tools before deploying to patients and clinicians. Ongoing monitoring catches performance drift. Audit records that support internal AI governance and ongoing monitoring, consistent with the Joint Commission/CHAI Guidance on the Responsible Use of AI in Healthcare (September 2025).
- Vendor AI validation before deployment
- Ongoing monitoring for performance drift
- Audit trail documentation for internal AI governance
- Reduce malpractice exposure from AI-assisted decisions
For CROs and Pharmaceutical Companies
Audit AI-generated clinical trial documentation and validate AI in endpoint adjudication workflows. Create regulatory-grade audit evidence for electronic records.
- Clinical trial documentation audit
- Endpoint adjudication AI validation
- Regulatory-grade audit trails
Why Healthcare AI Needs Purpose-Built Auditing
VIGIL's flag set is built for healthcare-specific failure modes:
- Clinical hallucinations where the AI generates plausible but incorrect medical information
- Fabricated citations to non-existent studies or guidelines
- Scope violations where a triage chatbot begins providing diagnostic or treatment advice
- Documentation infidelity where AI-generated SOAP notes diverge from the source conversation
VIGIL was designed by a physician-scientist who understands which errors are clinically dangerous versus cosmetically imperfect. It detects 13 clinically specific flag types using multi-model consensus — because in clinical safety, false negatives are more dangerous than false positives.
The Regulatory and Guidance Landscape
Healthcare organizations deploying AI face a growing body of federal and state requirements, accreditation standards, and voluntary guidance.
Federal Requirements
- FDA — regulates AI that meets the device definition; the GMLP guiding principles include monitoring deployed models
- ONC — HTI-1 set transparency requirements for decision support interventions in certified health IT
State Laws
- California AB 3030 — effective January 1, 2025, requires a disclaimer on generative-AI patient communications about clinical information, unless a licensed provider reviews them
- Colorado SB 26-189 — signed May 14, 2026, repealed and replaced the Colorado AI Act with a narrower automated-decision-making-technology law effective January 1, 2027
Accreditation Standards
- Joint Commission — Joint Commission and CHAI released voluntary guidance on September 17, 2025; Joint Commission has announced a voluntary AI certification
Frequently Asked Questions
What is a healthcare AI audit?
A healthcare AI audit is a systematic evaluation of AI-generated clinical content — including patient conversations, chatbot responses, and clinical documentation — to detect safety violations, medical inaccuracies, prompt injection attacks, fabricated authorities, and compliance issues before they reach patients or become part of the medical record.
How does multi-model consensus reduce audit errors?
Multi-model consensus sends each audit to multiple independent AI systems that analyze the content separately. Each finding is cross-checked across independent models, and agreement is reported as a confidence level.
What types of AI safety violations can this system detect?
The system detects a comprehensive range of healthcare AI safety violations including prompt injection attacks, clinical hallucinations, fabricated medical authorities, scope violations, dosage safety errors, documentation integrity failures, transcript infidelity, unauthorized medical advice, and security threats — across 13 clinically specific flag types designed by a physician-scientist.
Is the audit system HIPAA compliant?
Yes. The system implements HIPAA Administrative, Physical, and Technical safeguards. All data is processed under a signed HIPAA Business Associate Agreement with zero data retention — content is held in memory only during the audit, never stored, logged, or used for model training. All data is encrypted in transit via TLS with web application firewall protection. Enterprise customers can request custom data handling agreements.
How does this differ from general AI governance tools?
VIGIL's flag set is built for healthcare-specific failure modes — fabricated guidelines, non-existent citations, and advice outside authorized scope — issues invisible to general-purpose platforms. 13 clinically specific flag types designed by a physician-scientist.
What AI models are used in the audit pipeline?
The pipeline uses multiple independent AI systems at different stages — rapid screening, deep clinical analysis, and cross-validation — so no single model determines the final audit result. All models run on HIPAA BAA-covered infrastructure with enterprise-grade security controls.
Can this integrate into our CI/CD pipeline?
Yes. The system exposes a REST API that accepts conversation transcripts and clinical notes, returning structured JSON audit results. Integrate it into CI/CD workflows to automatically audit AI-generated content before deployment, with batch processing for high-volume enterprise use cases.
Which regulations and guidance relate to healthcare AI monitoring?
FDA regulates AI that meets the device definition; the GMLP guiding principles include monitoring deployed models. ONC HTI-1 set transparency requirements for decision support interventions in certified health IT. California AB 3030 (effective January 1, 2025) requires a disclaimer on generative-AI patient communications about clinical information. The Joint Commission and CHAI released voluntary guidance on the responsible use of AI in healthcare in September 2025.
Ready to Audit Your Healthcare AI?
Try the audit system with sample data to see it in action, or request an enterprise demo for batch processing, API integration, and custom reporting.