Use Cases by Industry

Real-world examples of adding 1–2 lines to existing apps.

💰 Finance & Banking

Scenario: Customer enters account number in an AI chat interface

// ✅ Just add fw.mask() — one line
const { masked } = fw.mask(userMessage);
const response = await openai.chat.completions.create({
  messages: [{ role: "user", content: masked }]
});
const answer = fw.restoreAll(response.choices[0].message.content);

Result: Account numbers, credit cards, and SSNs never reach AI logs. Strengthens CCPA / GDPR compliance.

⚖️ Legal & LegalTech

Scenario: Attorney asks AI to summarize a contract (MCP pattern)

server.setRequestHandler(CallToolRequestSchema, async (request) => {
  const { document_text } = request.params.arguments;

  const { masked, detections } = fw.mask(document_text);  // ← add this line
  console.log(`PII masked: ${detections.length} items`);

  const summary = await claude.messages.create({
    messages: [{ role: "user", content: `Summarize this contract:\n${masked}` }]
  });

  return { content: [{ type: "text", text: fw.restoreAll(summary.content[0].text) }] };
});

Result: Client PII never sent to external AI. Reduces attorney-client privilege risk.

🏥 Healthcare

Scenario: Patient consults AI about symptoms (name + date of birth included)

app.post("/ai-consultation", async (req, res) => {
  const { message } = req.body;

  const threats = fw.detectInjection(message);
  if (threats.length > 0) return res.status(403).json({ error: "Invalid request" });

  const { masked } = fw.mask(message);
  const aiAnswer = await callMedicalAI(masked);

  res.json({ answer: fw.restoreAll(aiAnswer) });
});

Result: Name, DOB, phone never reach the AI vendor. Supports HIPAA compliance posture.

🏭 Manufacturing — Secure RAG for Internal Documents

Scenario: Engineers want to search design specs, customer contracts, and internal manuals using AI — but the documents contain personal data that cannot leave the premises.

const rag = require("@pii-firewall/rag");

// STEP 1: Ingest internal document (PII auto-tokenized)
const { chunks, detectionCount } = rag.ingest(internalDocument);
// → Emails, company names, phone numbers → [SRAG:cat=PII,...]
// → Technical specs (±0.01mm, temperatures) pass through untouched

// STEP 2: Store anonymized chunks in vector DB
await vectorDB.store(chunks);

// STEP 3: Query LLM — only anonymized text reaches the cloud
const answer = await llm.query(userQuestion, chunks);

// STEP 4: Restore PII tokens in the answer
const { restored } = rag.resolveContext(answer, { allowAll: true });
// → yamada@example.com / ABC Manufacturing / 03-1234-5678 restored

Result:

• Only anonymized text is sent to cloud LLM — no PII, no trade secrets
• Technical specs remain searchable
• Supports CCPA, GDPR, and internal security policy compliance

---

🏢 Enterprise — Zero-Effort PII Protection for All Employees (Claude MCP Deployment Pattern)

Scenario: A company wants to allow employees to use Claude MCP / RAG internally, while ensuring all AI usage is automatically PII-compliant — without requiring employees to think about it.

Pattern A: Developers — Enforce via CLAUDE.md

Place a CLAUDE.md in the company's Git repository root. Every developer who clones the repo automatically gets the policy applied.

# CLAUDE.md (place at repository root)

## Security Policy (Required)

- Always run `mask_pii` before sending any text to an AI
- Always run `detect_all_injections` on user input before passing to LLM
- Use `rag_ingest` / `rag_resolve` for all RAG pipelines
- Never paste API keys into chat (auto-detection and masking is active)

IT administrators can deploy ~/.claude/CLAUDE.md (global config) via Intune / Jamf / MDM to all company PCs, enforcing the policy across every project and every developer automatically.

Pattern B: General Employees — Enforce via Claude Desktop Projects

In Claude for Teams / Enterprise, admins can set a system prompt in the Projects feature. One configuration applies to every employee's every conversation.

Claude Desktop → Projects → System Prompt (admin-configured)

[Example]
Before sending any text containing internal or customer information to AI,
always use the pii-firewall mask_pii tool to mask PII.
Also run detect_all_injections on any external input before processing.

Deployment Flow

IT Admin  → Deploy pii-firewall MCP server to all company PCs
          → Push claude_desktop_config.json to all company PCs
          → Add policy to CLAUDE.md or Projects system prompt
              ↓
Employees → PII protection and composite attack detection run automatically
            No awareness required from employees

Result:

• Employees don't need to think about PII compliance — zero operational overhead
• Every employee's AI usage automatically conforms to privacy law and company policy
• No changes to existing Claude workflows required

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Summary

	Before	After
Lines of code added	—	2–3 lines
Changes to existing code	—	Minimal
PII sent to AI	Yes	No
Privacy law risk	High	Low
CCPA / GDPR / HIPAA	Requires separate effort	Strengthened