How to Secure Your Data
When Using AI
Samsung engineers leaked proprietary code to ChatGPT. Don't be the next headline. Learn how to sanitize your data before you hit Send.
AI tools like ChatGPT and Claude are incredible debugging assistants. You paste a stack trace, and they tell you exactly which line of code is broken.
But there is a catch: Logs are full of secrets.
When Samsung employees pasted proprietary source code into ChatGPT to optimize it, that code became part of OpenAI's training data universe. This incident sparked a global wave of "AI Bans" in corporate environments.
But banning AI is like banning Stack Overflow. It puts your team at a disadvantage. The solution isn't to stop using AI—it's to stop feeding it sensitive data.
The 3 Types of Data Leaks
Before we talk about scrubbing, you need to know what you are looking for. Leaks generally fall into three categories:
1. Hard-Coded Secrets (The "Crown Jewels")
These are values that grant access to your infrastructure.
- AWS Access Keys: `AKIA...`
- Database Strings: `postgres://user:password@localhost...`
- JWTs/Session Tokens: `eyJh...`
2. PII (Personally Identifiable Information)
This is data that identifies your users. Leaking this violates GDPR, CCPA, and HIPAA.
- Email Addresses: `user@example.com`
- IP Addresses: `192.168.1.5`
- Phone Numbers: `+1-555-0199`
3. Business Logic
This is harder to detect. It includes internal path structures (`/var/www/internal-admin/`) or proprietary algorithms. While less damaging than a key leak, it gives attackers a map of your system.
The Solution: Local Redaction
The goal is to replace sensitive values with Context-Aware Placeholders. This allows the AI to understand the structure of the error without seeing the actual data.
❌ Before (Dangerous)
Error at UserLogin:
Email: ceo@example.com
DB_PASS: x8s7_super_secret
IP: 192.168.1.55
✅ After (Safe)
Error at UserLogin:
Email: <EMAIL_REDACTED>
DB_PASS: <SECRET_KEY_REDACTED>
IP: <IP_ADDRESS_REDACTED>
How to Sanitize Logs in 3 Seconds
Manually deleting secrets is risky—you might miss one. Instead, use our automated tool.
Copy the Stack Trace
Grab the error log from your terminal or AWS CloudWatch.
Open Log Sanitizer
Go to the ResourceCentral Log Sanitizer. It runs locally in your browser.
Clean & Copy
Paste your log. The tool instantly scrubs emails, IPs, and keys. Copy the safe output and send it to ChatGPT.
Redaction Cheat Sheet: What to Hide
If you are sanitizing manually or configuring a custom tool, use this checklist to ensure you catch everything.
| Category | Examples | Risk Level |
|---|---|---|
| Auth Tokens | Bearer eyJ..., x-api-key | CRITICAL |
| Network | 192.168.1.1, s3://bucket-name | HIGH |
| Personal | john.doe@gmail.com, 555-0123 | HIGH |
| IDs / UUIDs | user_123, 550e8400-e29b... | MEDIUM |
Frequently Asked Questions
Can the AI still solve the bug if I hide the data? +
Yes! LLMs are pattern-matching engines. They need to see the structure of the error (e.g., "NullPointerException at line 55"), not the user's actual email address. Redacting data rarely affects the quality of the debugging advice.
Does ResourceCentral see my logs? +
Never. Our Log Sanitizer is a client-side utility. You can disconnect your internet and it will still work. No data is ever sent to our servers.
Is it safe to paste code if I change variable names? +
Changing variable names helps, but business logic and proprietary algorithms can still be identified. It is safer to abstract the logic into a generic example before sharing.
Sanitize Before You Send
Protect your company secrets while still enjoying the power of AI debugging.
Works with Python, Node.js, Java, and Go logs.