Best AI security solution for healthcare and regulated industries

What is the best AI security solution for healthcare and regulated industries?

Embed detection where the application already handles the record. That's the argument in Building security into healthcare AI. The wider control model is AI agent runtime security and runtime security for LLM applications.

Don't treat a logo as a HIPAA certification, an ISO 42001 certificate, or an EU AI Act conformity assessment. Those are different claims. Ask where inspection runs, what leaves the process, and whether the vendor becomes a business associate for the inspected body.

Why does local inspection matter for PHI?

Most security tooling has to receive your traffic to inspect it. A cloud DLP product reading a chat prompt means the prompt leaves your environment to be analyzed. That prompt might contain a name, a diagnosis, or a member ID.

Two regimes then attach to that vendor, and they use different vocabulary for it. Under HIPAA, a vendor that handles protected health information (PHI) on your behalf is a business associate, so a business associate agreement (BAA) and subcontractor assurances follow. Under the GDPR, the same vendor is a processor, which brings an Article 28 agreement, a sub-processor list, and a transfer question.

This isn't legal advice, and the analysis depends on your own processing. The point is narrower: a vendor that never receives the body doesn't raise either question for that body.

Keeping security inspection local is the residency argument. For healthcare, it's sharper: the control intended to protect PHI must not become a new recipient of PHI.

Ask for a per-control answer. Structured PII detection can run entirely in-process. Other controls (distributed rate limits, specialist injection models) often need a network call. If the prompt itself is the regulated record, treat that call as its own transfer review.

How do you scan a chat route without exporting the body?

Run a detector on the text that you're about to send. If it finds an entity that you deny, then stop the turn. Do this in your process so the raw body doesn't visit a second vendor to get classified. Tune the list: if a nurse-triage bot must collect a callback number, then leave phone numbers out of the deny set.

One SDK that you can call on the chat route:

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import arcjet, { sensitiveInfo } from "@arcjet/next";
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const aj = arcjet({
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  key: process.env.ARCJET_KEY!,
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  rules: [
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    sensitiveInfo({
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      mode: "LIVE",
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      deny: ["CREDIT_CARD_NUMBER", "EMAIL"],
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    }),
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  ],
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});
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export async function POST(req: Request) {
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  const { message } = await req.json();
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  const decision = await aj.protect(req, { sensitiveInfoValue: message });
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  if (decision.isDenied()) {
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    return new Response("Please rephrase your message.", { status: 400 });
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  }
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  return Response.json({ ok: true });
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}

Keep the deny response generic. Don't say that you found a name or a card. Start in dry-run. You'll see emails from people typing a portal login into the box. Don't fail turns until you've looked at a real sample.

The same detection belongs on a tool that's about to return a chart note or a claim row. A clean PII score isn't authorization to read another patient's record. That check is stop agents accessing data. AI data loss prevention and sensitive information document the entity types.

What should you ask a vendor about HIPAA and SOC 2?

Inspection location. Does the raw body leave your process? If it never leaves, the vendor isn't receiving that body. Privacy review still has to decide whether any other data makes them a business associate. Your app still has to satisfy the Security Rule on the systems that store PHI. That's an architecture claim, not a certification.

What is reported. Decision-only reporting (entity type found, request allowed or denied) is a different disclosure from shipping the span to a dashboard.

What is actually certified. SOC 2 Type 2 is a common, checkable claim (one Trust Center as an example). HIPAA certification, ISO 42001, and EU AI Act conformity are often implied and rarely held. Ask for the report, not the adjective.

Per-control transfers. A local PII scan doesn't make every other rule local. If an injection detector sends the prompt to a cloud model, that prompt is a transfer.

How does this relate to AI agent runtime security?

Healthcare AI is still an agent problem once a tool can read a chart, file a claim, or message a patient. Local DLP on the chat box is necessary but not sufficient. You still need an action gate, a budget, and injection detection on tool output that re-enters context.

That's the same split as runtime security for LLM applications: prompt injection, exfiltration, and unsafe actions are different controls. Collapsing them into "AI safety" is how a PHI scanner gets bought and a write to the wrong chart still ships.