AI governance in healthcare is the set of policies, oversight structures, and technical controls that ensure AI tools (clinical, administrative, and vendor-embedded) are used safely and in compliance with HIPAA, FDA, and state AI laws. It spans the full lifecycle: discovering what AI is in use, approving it, testing it, and enforcing policy on every interaction, not just writing rules down.

A policy is a document describing how AI should be used. Enforcement is the technical layer that applies that policy to every AI interaction in real time, blocking violations before they happen. Most health systems have the first and not the second, which is why they can't prove AI use is compliant when a regulator, auditor, or plaintiff's attorney asks.

No. Traditional DLP and firewalls inspect data moving across known channels — email, USB, uploads — but can't evaluate what an AI model does with data inside a legitimate, encrypted session. They can't tell whether a chatbot generated a diagnosis it shouldn't have, or an AI scribe fabricated a consent note. That requires a governance layer built to inspect AI prompts and responses directly.

Standard consumer versions of ChatGPT, Copilot, Gemini, and related tools are not HIPAA compliant and cannot be used with PHI, because there's no Business Associate Agreement in place. Enterprise or healthcare-specific tiers can be made compliant only if a BAA is signed and the organization enforces policy on how the tool is actually used. The BAA alone doesn't guarantee compliant behavior.

Shadow AI is any AI tool used inside a health system without IT, security, or compliance approval, most often a clinician pasting patient data into a public chatbot to save time. Recent surveys put shadow AI usage at 40–57% of healthcare professionals having encountered or used an unauthorized tool, and most of it is invisible to standard security tools.

An AI guardrail is an automated rule that inspects an AI interaction and allows, modifies, or blocks it based on policy — for example, stopping a chatbot from issuing a diagnosis, or blocking PHI from reaching an unapproved model. Healthcare guardrails typically span categories like clinical scope, PHI protection, coding integrity, and regulatory compliance.

Most health systems align AI governance with the NIST AI Risk Management Framework, HIPAA, CMS guidance, FDA requirements for AI/ML-based devices, and a growing list of state AI governance laws. A defensible program maps every enforcement decision and audit log directly back to these frameworks, rather than treating compliance as a separate exercise.

Vitea Command is the enforcement layer of the Vitea AI governance platform for healthcare. It applies 100+ healthcare-specific guardrails to every AI prompt and response in real time, blocking unsafe outputs before they reach a clinician, the EHR, or a patient, and logging every decision for audit and compliance reporting.

DLP and CASB tools govern where data moves. Vitea Command governs what AI is allowed to do. It reads the clinical and compliance context of a prompt or response, not just whether sensitive data crossed a boundary, catching things DLP structurally can't, like a hallucinated diagnosis or an out-of-scope treatment recommendation.

Most health systems have guardrails active within months. Command connects to existing infrastructure (for example, a forward proxy, ICAP integration with tools like Zscaler or Palo Alto, a browser extension, or an API/LLM gateway) so there's no rip-and-replace of your current security stack.