The AI Questions Healthcare Buyers Are Asking: Inside a Governance Committee
A vendor demo just got stopped cold by a single question.
I watched it happen from inside the room.
A few months ago, I joined a healthcare governance committee that meets regularly to evaluate AI vendors as part of their transformation strategy.
In one of our recent sessions, a vendor came up for review.
The product had a valid use case.
The demo was clean.
Then two questions surfaced.
Did the vendor have a BAA in place?
What controls did they have to prevent their own workforce from entering PHI into the system?
The room paused.
Nobody on the committee had thought to ask those questions early on.
They came up during discussions and no one knew the answers.
That's the inside view you never get as a health tech vendor.
The questions healthcare buyers are asking aren't always technical.
And they're getting sharper every meeting.
The rest of this issue covers what's happening inside these rooms, and what it means for your next deal.
What governance committees discuss
Healthcare governance committees have expanded what they evaluate.
Vendors are now being assessed as extensions of their own workforce.
This changes the discussion completely.
A few months ago, the conversation in these rooms was: "Does this AI tool work?"
Today the conversation is: "If we let our staff use this tool, what's the risk that PHI ends up somewhere it shouldn't?"
That question lives in governance, not product.
And it's the question that decides whether you're in the conversation or not.
The fear inside healthcare governance committees now is that staff will use AI in ways the vendor didn't anticipate.
Pasting clinical notes.
Uploading care plans.
Running consumer-grade tools because they're free, convenient and fast.
Healthcare buyers are responding by raising the bar on every AI vendor they evaluate.
Three things changed:
The yes/no AI gate now exists. "Do you use AI in your product?" is now a question that determines whether more extensive due diligence is necessary.
Vendor scrutiny extends to the vendor's own workforce. Buyers want to know how your engineering and customer success teams handle PHI when they're using internal AI tools.
HIPAA Compliance became the ground floor, not the ceiling. A documented compliance program is the entry requirement. Trustworthy AI governance is what closes the deal.
👉 If you want to see how your AI governance scores against what healthcare buyers are evaluating, the Health Tech AI Readiness Self-Assessment walks you through it section by section. Get your score here.
Why AI expands your HIPAA Compliance footprint
The moment AI touches PHI, your compliance scope expands in ways many health tech vendors haven't accounted for.
AI inputs are PHI.
AI outputs can become PHI.
Training data lineage matters under HIPAA.
Model access controls fall under HIPAA's technical safeguards.
The blind spot health tech companies have isn't the AI itself.
It's where AI governance lives inside the company.
Typically, AI oversight sits inside the product team.
The engineers who built the model also decide how it's monitored.
Compliance reviews happen quarterly, if at all.
Healthcare buyers see this as an operational risk.
AI governance belongs inside your compliance program, not your product roadmap.
This operational shift changes how buyers evaluate you.
The four questions that decide if you're in the conversation
These are the questions I'm hearing most often in committee discussions about vendors with AI features.
If you can't answer all four with documented evidence in a reasonable time, you're not ready for the next round of questionnaires.
1. How is the model validated?
Not how it was trained. How it's tested for accuracy, drift, and bias on an ongoing basis.
2. How is model drift managed?
When the model's performance shifts over time, accuracy degrades, outputs change, and behavior drifts from baseline. Who monitors it, how is it updated, and how fast? Drift in a clinical context is a patient safety question and one of the main reasons AI ends up on shelves.
3. Where did the training data originate?
Was PHI used in training? Was the data de-identified? Is the lineage documented? Healthcare buyers will ask. They're being asked the same question by their boards.
4. Who has access to model endpoints?
Inside your company and outside. Vendors, contractors, third-party integrations. Every endpoint is a potential access point to PHI.
If those questions caught you off guard, that's your gap.
👉 If those four questions exposed a gap, the Health Tech AI Readiness Self-Assessment maps it for you. 48 questions across problem definition, vendor selection, and implementation. You'll know exactly where you stand before your next questionnaire. Take the assessment.
HIPAA Compliance is the gate. AI governance is the differentiator.
Healthcare buyers aren't choosing between vendors based on AI features.
They're choosing based on which vendor they trust to handle their data without becoming a liability.
HIPAA Compliance gets you in the room.
AI governance closes the deal.
The vendors winning contracts right now have one thing in common.
They walked into the security review with documented answers, a defensible compliance program, and an AI governance framework that lives outside the product team.
They didn't get lucky.
They built for this moment before the questions were asked.
What this means for the next 90 days
Healthcare procurement cycles are tightening.
Governance committees are meeting more often.
Boards are asking sharper questions.
Security teams are adding AI-specific sections to vendor questionnaires.
The vendors who wait until they're asked will lose deals to the vendors who already have the answers.
The window to get ahead of this is now.
If your AI governance lives inside product, move it.
If your HIPAA Compliance program is informal, document it.
If your team can't answer the four questions above with evidence, build the evidence before your next questionnaire arrives.
👉 If you're building toward enterprise health system deals and want to know exactly what they're evaluating in AI vendors, take the Health Tech AI Readiness Self-Assessment. It's the same framework I use when evaluating vendors on the governance committee.
Get your score here.
- Larry | Founder & Principal CISO, Inherent Security
FAQ Section — HIPAA Compliance for AI Vendors
Q: What is HIPAA Compliance for AI vendors?
HIPAA Compliance for AI vendors means meeting the same Privacy and Security Rule requirements as any other business associate handling Protected Health Information (PHI), with additional considerations for how AI systems process, store, and learn from that data. This includes documented policies, signed Business Associate Agreements, technical safeguards on model access, and risk analysis covering AI-specific exposure points like training data lineage and model outputs.
Q: What do healthcare buyers evaluate in AI-enabled vendors?
Healthcare buyers evaluate four things: how the model is validated for accuracy and bias, how model drift is monitored and managed, where the training data originated, and who has access to model endpoints. They also assess whether the vendor's own workforce has controls preventing PHI from being entered into unapproved AI tools. HIPAA Compliance is the entry requirement; AI governance is the differentiator.
Q: Is HIPAA Compliance enough for AI vendors?
No. HIPAA Compliance is the gate that lets you into the conversation, but it does not address AI-specific risks like model drift, training data lineage, or workforce misuse of generative AI tools. Healthcare buyers expect AI vendors to demonstrate both HIPAA Compliance and a defensible AI governance framework that lives outside the product team.
Q: How is AI governance different from HIPAA Compliance?
HIPAA Compliance governs how a vendor handles PHI across all systems and processes. AI governance specifically addresses how AI models are validated, monitored, updated, and controlled throughout their lifecycle. The two overlap on data handling and access controls, but AI governance covers risks HIPAA was not written to address — including model behavior, training data sources, and ongoing performance monitoring.
