The Problem
"We have 50+ AI vendors knocking on our door. Their security packets all say the right things. What we do not have is evidence that their AI controls actually run when the workflow is live."
— Health System CISO
No Time
Security teams are stretched thin. Every new AI vendor means another set of claims to reconcile against clinical workflow risk.
No Runtime Proof
Traditional packets show policies and infrastructure controls. They rarely prove what happened inside the AI workflow at decision time.
Real Review Pressure
Clinical, compliance, privacy, security, and procurement teams need a common artifact: what controls ran, what they decided, and whether protected data stayed protected.
Beyond Questionnaire Review
A good AI vendor review asks for the runtime evidence a health system will need later: intake, control requirements, receipt generation, evidence review, and action.
Intake
Identify the exact clinical workflow, data boundary, tools, user roles, and decision points before a pilot becomes operational dependency.
Control Requirements
Define what must be blocked, escalated, logged, verified, and kept local for that vendor workflow to pass review.
Signed Receipts
Require evidence that controls ran in the live workflow without exposing PHI, prompts, secrets, or patient-specific payloads.
Evidence Review
Use an evidence pack that maps receipts to HIPAA, state AI laws, SOC 2, internal policies, and clinical safety requirements.
Action
When risk is found, act on a specific control, workflow, or vendor obligation instead of reopening a generic questionnaire.
How GLACIS Helps
Runtime Evidence Requirements
We help you translate vendor AI risk into evidence requirements attached to a real workflow, not a generic AI policy packet.
- Workflow-specific control requirements
- OVERT, ATLAS, OWASP, HIPAA, and internal-policy mappings
- Receipt fields reviewers should require
Evidence Pack Pattern
Know what proof to demand from vendors. Policy docs are useful, but the review should hinge on evidence that controls actually ran.
- Signed runtime receipts
- Control coverage and exception summaries
- Zero sensitive-data-egress proof
Reference Sprint
Start with one vendor workflow and create the evidence pattern other departments can reuse.
- Runtime surface map
- Local control placement
- Buyer- and auditor-readable evidence pack
Operational Follow-through
AI governance is not a one-time document. Regulations change, vendor models update, and clinical workflows drift. Evidence should show where to act next.
- Regulatory updates tied to runtime evidence
- Vendor re-assessment triggers
- Control improvements from receipt trends
What’s Coming
State and federal regulators are moving fast. Health systems that deploy AI without proper governance are taking on significant liability.
| Regulation | Impact | Date |
|---|---|---|
| Colorado AI Act | Impact assessments for high-risk AI in healthcare | June 2026 |
| Texas HB 1709 | Written disclosure to patients when AI used | Jan 2026 |
| EU AI Act | High-risk classification for most healthcare AI | Aug 2026 |
| HHS HIPAA Update | AI systems must be in risk analysis | Proposed |
Build the evidence requirement before the next vendor review
Pick one high-risk AI workflow. GLACIS will help map the runtime surface, define local controls, and assemble the evidence pattern your review process can reuse.
Talk to the team