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HIPAA + AI — PHI in LLM Prompts

HealthcareHIPAAPII Masking

Using OpenAI, Anthropic, or any LLM API with patient data (PHI) without a signed Business Associate Agreement (BAA) is a HIPAA violation. Most AI providers only offer BAAs on enterprise tiers costing $20,000+/year. Every hospital IT team quietly using ChatGPT with patient context is at risk.

⚠️
Maximum Penalty
$100 – $1.9 million per violation category per year
Affects: Any covered entity (hospital, clinic, health plan) or business associate using LLM APIs in workflows that touch PHI.

The Developer Problem

Healthcare developers building AI features — clinical summaries, care gap analysis, documentation assistance — are sending PHI to OpenAI/Anthropic APIs without realising this violates HIPAA. The solution isn't an enterprise contract; it's masking the PHI before it ever leaves the application.

What You Must Build

These are the exact technical components regulators will check for:

1

PII/PHI detection and masking before prompts are sent to any LLM API

2

Re-injection of masked values into AI responses before showing to users

3

Audit log of what PHI was detected and masked in each request

4

BAA (Business Associate Agreement) with any AI vendor that processes PHI

Consequences of Non-Compliance

Fines $100–$1.9M per violation category per year

HHS OCR investigation and corrective action plan

Public breach notification if >500 records affected

Personal liability for executives under HIPAA criminal provisions

Drop-In Code Solution

Instead of building this from scratch (2–6 weeks of engineering time), use this production-ready package that implements all the required components above.

🔒
Zero-Trust Edge PII Masking Middleware
Let your developers use AI without leaking customer data to OpenAI or Anthropic.
$79
/month
⏱ Setup: 30 minutesNext.jsTypeScriptVercel EdgeNode.js

A lightweight TypeScript middleware wrapper. It intercepts the prompt at the Edge before it leaves your network, uses optimized local regex + lightweight NER to identify and hash all sensitive data, sends the clean prompt to the AI API, and decodes hashes back into real values only inside the secure browser context.

Code Preview
// pii-masker.edge.ts — intercept before prompt leaves network
import { maskPII, unmaskPII } from './pii-masker.edge'

// In your Next.js API route:
export async function POST(req: Request) {
  const { prompt } = await req.json()
  
  // Mask PII before sending to OpenAI
  const { cleanPrompt, vault } = await maskPII(prompt)
  
  const response = await openai.chat.completions.create({
    messages: [{ role: 'user', content: cleanPrompt }]
  })
  
  // Restore real values only for the user's browser
  const safeResponse = unmaskPII(response.choices[0].message.content, vault)
  return Response.json({ content: safeResponse })
}
Files Included
pii-masker.edge.ts
pii-decoder.client.ts
patterns/financial.ts
patterns/healthcare.ts
patterns/general.ts
README.md
LICENSE
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