Research Portfolio
AI governance research, standards work, and technical artifacts
NHID-Clinical is an operational AI governance framework for non-human actor accountability — the disclosure, delegated authority, and auditability of AI-operated interactions in healthcare AI workflows. This page gathers the research, live demonstrations, evidence, and profile behind it.
Standards & Regulatory Engagement
Research & credibility
Public standards engagement, and how the framework maps to recognized governance references. Mapped, not certified.
NIST AI RMF — public comment
Public comment submitted to the NIST AI RMF docket (NIST-2025-0035-0026). A public comment, not a NIST endorsement, adoption, or certification.
Read the comment on regulations.gov →NHID-Clinical v1.3 specification
The full control set — IDG-01, PDX-01, DBC-01, EIT-01, ATR-01 — and the event schema for disclosed non-human actors.
Read the specification →Regulatory alignment
How the framework maps to EU AI Act Article 50 transparency obligations, NIST AI RMF, and ISO/IEC 42001. Mapped, not certified.
See the alignment →AI Governance Map
Interactive resource tracking enacted AI legislation across U.S. states and jurisdictions.
Open the AI Governance Map ↗Interactive Demonstrations
Technical demonstration
Working software you can run: the conformance controls, the live API, and the open repository.
Governance simulator
Run the v1.3 controls against call scenarios in real time.
Open the simulator →Live conformance API
Interactive API demo — send a call payload and get a deterministic pass/fail result. No key for demo routes.
Try the live API →GitHub repository
The engine, conformance test suite, adapters, and reference code — open under CC BY 4.0.
View the repository ↗Technical stack
The five-layer trust stack and how the pieces compose with FHIR, OAuth, and IAM.
Explore the stack →Implementation registry
Self-attested implementations with conformance results. No external authority validates results.
Browse the registry →Evidence & Publications
What has been released
Released artifacts and public documents. Everything here is self-attested and open for review.
Evidence Pack
System-behavior guarantees, a worked failure trace, and the audit-readiness model for evaluation teams.
Open the evidence pack →Executive Brief
One-page overview for hospital, payer, compliance, and procurement leaders.
Read the brief ↗Technical Proof Package
Deterministic guarantees, the audit-readiness model, and architectural properties of the reference implementation.
Review the proof →Tier 0 Shadow Pilot Kit
Released kit: measure impersonation latency on your own call logs in 2–4 weeks — observe-only, no vendor changes.
Get the pilot kit ↗Why Identity Is the Missing Layer of Responsible AI
Independent commentary on Impersonation Latency and the five NHID-Clinical controls, mapped to NIST AI RMF, ISO/IEC 42001, and HIPAA.
Read the commentary →Professional Profile
Brianna Baynard
Creator of NHID-Clinical, built from direct payer operations experience on live eligibility, claims, and prior-authorization lines. Full profile and background on the professional portfolio and LinkedIn.
About NHID-Clinical
The origin of the framework and the operational problem behind it.
Read the story →Completed certifications
Published research & public artifacts
NHID-Clinical
Non-Human Identity Disclosure controls for healthcare voice workflows — specification, reference implementation, and open repository.
nhid-clinical.org ↗NIST AI RMF — public comment
Submitted public comment (NIST-2025-0035-0026) with recommendations on healthcare AI safety controls for voice-agent workflows. A public comment, not a NIST endorsement.
Read the comment ↗AI Governance Map
Interactive resource tracking enacted AI legislation across U.S. states and jurisdictions.
Open the map ↗NHID-Clinical is a voluntary, open framework — not an accredited standard, certification, or regulatory requirement. It maps to EU AI Act Article 50 transparency obligations and NIST AI RMF 1.0; it does not claim compliance or certification. The NIST link is a public comment, not a NIST endorsement.
Next step
Read the specification or start a shadow pilot.
Observe-only, on your own call logs. No vendor changes, no production risk.