If you’re building with OpenAI, Anthropic, or any external LLM API, there’s one risk that gets ignored too often:
private user data leaking into prompts.
That usually doesn’t happen because of bad intent. It happens because developers move fast, prompts get assembled dynamically, logs get copied into AI tools, and suddenly emails, phone numbers, credit cards, IDs, or tokens are being sent outside your system.
That’s why I built Personal Information Remover.
It’s a lightweight tool that scans raw text and masks sensitive information before it gets sent to an AI model.
What it does
Personal Information Remover is designed to be simple and practical.
It can detect and mask:
- email addresses
- phone numbers
- credit card numbers
- SSNs
- IBANs
- API keys
- tokens
- credentials
- private keys
- and many other sensitive text patterns
The goal is straightforward: put a safety layer between your app and external AI APIs.
Why this matters
A lot of AI teams focus on prompt quality, model latency, and cost.
But privacy mistakes are often much more serious than prompt mistakes.
If customer information, internal credentials, or personal identifiers leave your environment unintentionally, you create legal, security, and trust problems very quickly.
A simple masking layer reduces that risk immediately.
Good use cases
This tool is useful when you are:
- sending support tickets to LLMs
- analyzing logs with AI
- summarizing customer conversations
- processing CRM or sales notes
- cleaning internal documents before AI enrichment
- building AI copilots on top of user-generated content
If there is any chance raw text may contain personal or sensitive data, this tool helps reduce the risk before the prompt goes out.
Install from npm
You can install it with npm:
npm install @remova/personal-information-remover
Or run it directly with npx:
npx @remova/personal-information-remover --text "Contact john@email.com at +1 415-555-2671"
You can also pipe text into it:
echo "Card 4242 4242 4242 4242" | npx @remova/personal-information-remover
Use it in code
Example:
const { maskSensitiveInfo } = require("@remova/personal-information-remover");
const input = "Email: john@email.com, Phone: +1 415-555-2671, Card: 4111 1111 1111 1111";
const output = maskSensitiveInfo(input);
console.log(output);
// Email: [EMAIL REMOVED], Phone: [PHONE REMOVED], Card: [CARD REMOVED]
You can also disable the extended regex database if you want a more limited masking pass:
maskSensitiveInfo("SSN 123-45-6789", { extended: false });
Why I like simple tools like this
A lot of security improvements do not need to start as giant platforms.
Sometimes the right first step is a small, reliable utility that developers can actually drop into their workflow today.
That’s what Personal Information Remover is meant to be.
Not a full compliance system. Not a giant privacy suite. Just a practical filter that helps stop obvious mistakes before they become expensive ones.
Important note
This tool is a strong first-pass masking layer, but it should not be treated as a complete enterprise privacy system on its own.
You still need proper governance, access control, auditability, and review processes if you’re handling sensitive data at scale.
Where to get it
Install:
npm install @remova/personal-information-remover
Run instantly:
npx @remova/personal-information-remover
Final thought
If you’re building AI features, privacy protection should happen before the API call, not after the mistake.
That’s the purpose of Personal Information Remover.
If you need stronger AI security, policy controls, and enterprise-grade protection around AI usage, visit www.remova.org.
If you need help integrating AI into your systems, products, or growth workflows, check out www.goexpandia.com.
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