PIIVault — Auto-Mask Customer Data Before It Hits Your LLM
Your n8n support agent is one bad prompt away from leaking customer SSNs, emails, and phone numbers to OpenAI. PIIVault sits between your workflow and any LLM call, strips the sensitive stuff, and gives you an audit log your compliance team will actually love.
Difficulty
intermediate
Category
Business Automation
Market Demand
Very High
Revenue Score
8/10
Platform
Web App
Vibe Code Friendly
No
Hackathon Score
6/10
Validated by Real Pain
— sourced from real community discussions
Developers running AI support agents on n8n are manually engineering prompts and hoping customer PII does not leak into LLM API calls — no dedicated middleware solution exists for this workflow stack.
What is it?
Every automation agency building AI support bots on n8n or Make faces the same quiet nightmare: customer PII flowing raw into LLM APIs with zero guardrails. PIIVault is a Node.js middleware layer that intercepts workflow HTTP calls, detects and tokenizes PII using regex plus NLP entity recognition, forwards clean payloads to Claude or GPT, then de-tokenizes responses before returning them. Ships with a dashboard showing redaction audit logs and a one-click compliance PDF. Buildable right now because the n8n HTTP node accepts custom middleware endpoints and Claude API is stable.
Why now?
The May 2026 wave of agencies shipping n8n AI support bots at scale has outpaced any tooling for PII governance — and the recent public disclosure of vulnerabilities in 12K n8n templates has put compliance on every agency owner's radar this month.
- ▸Real-time PII tokenization before LLM calls using regex plus NLP entity recognition.
- ▸De-tokenization of LLM responses so downstream workflow gets clean readable output.
- ▸Audit log dashboard with per-workflow redaction counts and exportable compliance PDF.
- ▸Drop-in n8n HTTP node config snippet so setup takes under 10 minutes.
Target Audience
Automation agencies and in-house n8n developers running AI support bots — estimated 40,000+ active n8n Cloud users as of May 2026.
Example Use Case
An agency running a Shopify support bot on n8n routes all customer emails through PIIVault, which strips order emails and phone numbers before Claude sees them, then de-tokenizes the reply — zero PII exposure, full audit log ready for SOC 2 review.
User Stories
- ▸As an automation agency owner, I want customer emails stripped of PII before hitting Claude, so that my clients pass their next compliance audit without switching vendors.
- ▸As an n8n developer, I want a drop-in HTTP endpoint I can paste into any workflow, so that I spend zero extra dev time on compliance plumbing.
- ▸As a compliance-conscious SaaS founder, I want an exportable PDF audit log of every redaction event, so that I can show my board we are GDPR-compliant without hiring a consultant.
Done When
- ✓PII Detection: done when a POST request containing a fake SSN and email returns a payload where both are replaced with tokens and the LLM never sees raw values.
- ✓Audit Log: done when the dashboard shows a timestamped row for every proxied request with redaction count visible without any manual refresh.
- ✓Compliance Export: done when clicking Export PDF generates a downloadable report listing redaction totals per workflow for the selected date range.
- ✓Billing: done when Stripe checkout completes and the workspace is immediately upgraded to paid tier with no manual intervention required.
Is it worth building?
$500/month × 10 agency customers = $5,000 MRR at month 3. $1,000/month enterprise tier adds $3,000 MRR by month 6.
Unit Economics
CAC: $80 via LinkedIn DMs and r/n8n posts. LTV: $5,400 (18 months at $300/month). Payback: 1 month. Gross margin: 88%.
Business Model
SaaS subscription
Monetization Path
14-day free trial, then $299/month starter (1 workspace), $599/month agency (5 workspaces).
Revenue Timeline
First dollar: week 3 via beta upgrade. $1k MRR: month 2. $5k MRR: month 4.
Estimated Monthly Cost
Supabase: $25, Vercel/Render: $20, Claude API for detection assist: $30, Stripe fees: $15. Total: ~$90/month at launch.
Profit Potential
Full-time viable at $8k–$15k MRR targeting 15–25 agency accounts.
Scalability
High — can expand to Make, Zapier, and direct API SDK for any LLM stack.
Success Metrics
Week 2: 3 beta agencies using it live. Month 2: $3k MRR. Month 4: 90% retention rate.
Launch & Validation Plan
DM 20 n8n agency owners on LinkedIn offering free 60-day beta in exchange for a testimonial and monthly feedback call.
Customer Acquisition Strategy
First customer: post a detailed breakdown of the 12K n8n template vulnerability audit on r/n8n and offer free PIIVault access to first 5 responders. Ongoing: r/n8n weekly posts, n8n community forum, LinkedIn targeting automation agency owners.
What's the competition?
Competition Level
Low
Similar Products
AWS Macie for S3 data, Microsoft Presidio for Python pipelines, Nightfall for SaaS — none offer a drop-in n8n HTTP proxy with audit UI.
Competitive Advantage
Purpose-built for n8n workflows, not a generic DLP tool — zero config beyond pasting one HTTP node URL.
Regulatory Risks
GDPR Article 25 (data minimization) makes this a compliance requirement for EU customers — actually a sales tailwind, not a risk. Ensure PIIVault itself stores no raw PII in audit logs.
What's the roadmap?
Feature Roadmap
V1 (launch): PII proxy, audit log, n8n snippet, Stripe billing. V2 (month 2-3): Make and Zapier support, custom redaction rules. V3 (month 4+): SOC 2 report export, team roles, HIPAA BAA option.
Milestone Plan
Phase 1 (Week 1-2): proxy endpoint plus PII detection live, tested with real n8n workflow. Phase 2 (Week 3-4): dashboard, Stripe billing, 3 beta agencies onboarded. Phase 3 (Month 2): 10 paying customers, audit PDF export shipped.
How do you build it?
Tech Stack
Node.js, Express, Claude API, Supabase, Stripe — build with Cursor for middleware logic, v0 for dashboard UI
Suggested Frameworks
compromise.js for NLP entity detection, Express for middleware, Supabase for audit logs
Time to Ship
2 weeks
Required Skills
Node.js middleware, NLP entity detection, Supabase RLS, Stripe billing.
Resources
n8n HTTP node docs, compromise.js docs, Anthropic API docs, Supabase quickstart.
MVP Scope
server/index.ts (Express app entry), server/middleware/piiDetect.ts (entity tokenizer), server/middleware/deTokenize.ts (response restorer), server/routes/proxy.ts (LLM proxy endpoint), server/routes/audit.ts (log retrieval), lib/db/schema.ts (Supabase schema), app/page.tsx (landing), app/dashboard/page.tsx (audit log UI), app/api/stripe/route.ts (billing), .env.example (required vars)
Core User Journey
Paste HTTP endpoint into n8n node -> send test request -> see redacted payload in dashboard -> enable audit logging -> upgrade to paid.
Architecture Pattern
n8n HTTP node -> PIIVault Express proxy -> tokenize PII -> LLM API -> de-tokenize response -> return to n8n -> log to Supabase.
Data Model
Workspace has many Workflows. Workflow has many AuditEvents. AuditEvent stores redaction count, timestamp, workflow ID — never raw PII.
Integration Points
Claude API for LLM proxying, OpenAI API as alternate target, Supabase for audit log storage, Stripe for billing, Resend for onboarding email.
V1 Scope Boundaries
V1 excludes: Make/Zapier support, custom regex rule editor, HIPAA BAA, team roles, mobile app.
Success Definition
A paying agency deploys PIIVault to a live client bot, passes a client compliance review citing the audit log, and upgrades to the agency tier without talking to the founder.
Challenges
Convincing agencies the compliance risk is real before they have an incident — most will not pay until a client complains. Distribution is the hard problem, not the tech.
Avoid These Pitfalls
Do not store any raw PII even temporarily — your whole value prop collapses if you leak what you promise to protect. Do not try to support every LLM on day one — nail Claude and GPT-4o first.
Security Requirements
Supabase Auth with magic link, RLS on all workspace tables, rate limiting 60 req/min per workspace, no raw PII stored anywhere in the system, GDPR data deletion endpoint required.
Infrastructure Plan
Express server on Render ($7/month), Next.js dashboard on Vercel (free tier), Supabase for Postgres plus auth, Sentry for error tracking, GitHub Actions for CI.
Performance Targets
100 DAU at launch, proxy endpoint under 200ms added latency, dashboard under 2s load, no caching needed at v1 scale.
Go-Live Checklist
- ☐Security audit: no raw PII in logs.
- ☐Stripe payment tested end-to-end.
- ☐Sentry error tracking live.
- ☐Uptime monitor on proxy endpoint.
- ☐Custom domain with SSL configured.
- ☐Privacy policy and DPA published.
- ☐3 beta agencies signed off.
- ☐Rollback plan: redeploy previous Render image.
- ☐Launch post drafted for r/n8n and LinkedIn.
First Run Experience
On first run: a demo workspace is pre-seeded with 5 sample audit events showing redacted emails and phone counts. User can immediately send a test POST to the proxy endpoint using the pre-filled curl command on the dashboard. No manual config required: demo mode works with a built-in test LLM echo server before any real API key is entered.
How to build it, step by step
1. Define API contract: POST /proxy accepts target LLM URL plus payload JSON. 2. Scaffold Express app with TypeScript using ts-node. 3. Install compromise.js and write piiDetect middleware that tokenizes names, emails, phones, SSNs. 4. Build deTokenize function that swaps tokens back in LLM response. 5. Create Supabase schema for audit_events table with workspace_id and redaction_count. 6. Add Stripe webhook handler for subscription creation and cancellation. 7. Build Next.js dashboard with Supabase client showing audit log table and compliance PDF export. 8. Write n8n HTTP node config snippet and paste into README. 9. Deploy Express server to Render, Next.js to Vercel, configure env vars. 10. Verify: send a test n8n payload containing a fake email and phone number and confirm dashboard shows redaction event with zero raw PII stored.
Generated
May 26, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.