Anonymize Data Before It Reaches the LLM
Every AI prompt, every RAG pipeline query, every LLM fine-tuning dataset — if it contains PII, it's a compliance risk. We remove it before it leaves your control.
The Risk: PII Inside AI Workflows
Large language models ingest everything you send them. Once PII enters an LLM, you lose control over how it is stored, processed, or surfaced.
- Training Data Contamination — Personal data may be absorbed into future model weights
- Prompt Logging — AI providers may log inputs for debugging, safety review, or model improvement
- Output Leakage — PII from one user's prompt can appear in another user's response
- No Delete Capability — Once data enters a model, there is no reliable way to remove it
- Cross-Border Transfer — AI API calls may route data through non-EU jurisdictions
Pre-Processing Pipeline
A deterministic pre-processing layer that strips all PII before data reaches any AI service — with reversible tokens for re-identification when authorized.
The AI never sees real names, emails, or sensitive data — it works with safe tokens that preserve context and meaning.
MCP Server — AI Safety Built Into Your IDE
The Model Context Protocol (MCP) integrates anonymize.solutions directly into Claude Desktop, Cursor, and VS Code. Every file, every code snippet, every conversation is automatically anonymized before the LLM sees it — with zero workflow friction.
Setup in 5 Minutes
Add the MCP Server to your AI tool's configuration file. One JSON block, one API key — protection starts immediately for every conversation.
Claude, Cursor, VS Code
Works with Claude Desktop, Claude Code, Cursor IDE, Windsurf, and any MCP-compatible AI assistant via Streamable HTTP transport.
Zero-Knowledge Processing
Your text passes through, gets anonymized, and returns — we never store, log, or access your original content. Even our team cannot see your data.
You write code or paste text → MCP Server intercepts → PII replaced with safe tokens → LLM processes clean data → response de-anonymized automatically.
Three ways to protect AI workflows
Choose the integration point that matches your workflow — or combine all three for end-to-end coverage.
MCP Server
For developers and technical teams. Integrates directly into your IDE so every file, snippet, and conversation is anonymized before the LLM processes it.
- Claude Desktop, Cursor, VS Code
- Automatic context anonymization
- 7 specialized MCP tools
- Controlled data release
Chrome Extension
For business users and non-technical teams. Protects AI chat inputs on ChatGPT, Claude, and Gemini with real-time PII interception and automatic response de-anonymization.
- ChatGPT, Claude, Gemini, Copilot
- Real-time input interception
- Automatic response restoration
- Seamless browsing experience
REST API
For pipelines and automated workflows. Programmatic integration into RAG ingestion, ETL processes, ML training pipelines, and any system that sends data to LLMs.
- RAG, ETL, ML pipeline integration
- Batch processing capabilities
- JSON request/response format
- JWT authentication + rate limiting
Where AI safety matters most
Every AI workflow that processes personal data needs a pre-processing layer. These are the most common scenarios.
AI Chatbot Protection
Customer service bots processing personal data — names, account numbers, addresses, health information. Anonymize inputs before the LLM generates a response, then re-identify tokens in the output for the agent.
LLM sees: "I'm [NAME_1], my account is [ACCOUNT_1]"
RAG Pipeline Safety
Anonymize documents before vector embedding so your knowledge base contains zero PII. When the retrieval-augmented generation system surfaces context, no personal data leaks into LLM prompts or responses.
Query → Retrieve clean chunks → LLM
AI Agent Workflows
Multi-step agent chains where data passes through multiple LLM calls, tool invocations, and external APIs. Each step is a potential exposure point. Anonymize at the entry gate, restore at the exit.
→ Step 3 → De-anonymize → Output
LLM Fine-Tuning Prep
Clean training datasets of all PII before fine-tuning. Ensure your custom model never memorizes personal data from training examples — a GDPR requirement for any EU-based AI model training.
→ Fine-tune model → Zero PII in weights
Why deterministic detection matters for AI
We use NLP + Pattern engines — not LLMs — to detect PII. This is a deliberate architectural choice with concrete benefits for AI safety.
The Problem with LLM-Based Detection
Some anonymization tools use AI models to detect PII. This creates a circular dependency: you send sensitive data to one LLM to protect it from another LLM. Our approach eliminates this paradox entirely.
Deterministic means: same input always produces the same output. No hallucinations. No missed entities because the model was "creative." No variation between runs.
Benefits for AI Workflows
- No Hallucination Risk — Pattern and NLP engines cannot invent false positives from imagination
- Reproducible Results — Run the same text twice, get the same anonymization — critical for audit trails
- Complete Audit Trail — Every detection has a confidence score, entity type, and position — fully traceable
- No Data Sent to Third-Party AI — Detection happens on our EU infrastructure, never via external AI APIs
- Sub-Second Processing — No model inference latency — pattern matching is instantaneous
Deterministic vs. AI-Based Detection
| Property | Our Approach | LLM-Based |
|---|---|---|
| Reproducibility | ✓ 100% | ✗ Variable |
| Hallucination Risk | ✓ Zero | ✗ Present |
| Audit Trail | ✓ Full | ✗ Limited |
| Data Exposure | ✓ None | ✗ To AI Provider |
| Processing Speed | ✓ Sub-second | Seconds |
| GDPR Compliance | ✓ Built-in | ✗ Depends |
KEY DIFFERENTIATORS
- Engines: Microsoft Presidio NLP + Regex Pattern
- Entities: 260+ types across 48 languages
- Methods: Replace, Redact, Mask, Hash, Encrypt
- Hosting: 100% EU infrastructure (Hetzner, Germany)
Need a comprehensive AI governance strategy?
anonymize.solutions handles the technical layer — PII detection and anonymization. For enterprise AI governance consulting, policy frameworks, risk assessments, and EU AI Act compliance, our parent company curta.solutions provides dedicated advisory services.
GOVERNANCE
Policy + Technology
Protect your AI workflows today
Every prompt containing PII is a compliance risk. Eliminate the risk at the source — before data reaches any LLM.