Every day engineers paste log files, stack traces, and config snippets into ChatGPT, Claude, or Gemini to get help debugging. It is fast, it works, and it leaks secrets constantly.
API keys, database connection strings, JWT tokens, and customer email addresses routinely end up in LLM prompts. Once they do, you have lost control of them — they may be stored in training pipelines, accessible to support staff, or exposed through a future data breach at the LLM provider.
What actually ends up in prompts
The most common sensitive values we see in logs and configs:
- API keys and tokens — AWS access keys, GitHub tokens, Stripe secret keys, OpenAI keys. These are the highest-risk category because they grant direct access to systems and can be used immediately if extracted.
- Database connection strings — Often contain usernames, passwords, and hostnames in a single line:
postgres://admin:s3cr3t@db.internal:5432/prod. - JWT tokens — Long base64 strings that carry user identity claims. A leaked JWT is effectively a stolen session.
- Customer PII — Email addresses, phone numbers, and national IDs from application logs or exported CSVs shared for analysis.
- Private keys — PEM-encoded RSA and SSH private keys occasionally appear in bug reports when engineers include full config outputs.
Why "just be careful" does not work
Manual review before pasting is error-prone at speed. A log file has thousands of lines. A stack trace might have a database URL buried in a connection error on line 847. You will miss things, especially under pressure.
The right answer is automation that runs before the data leaves the device — not a policy that relies on every engineer making the right call every time.
The local-first approach
RedactOps scans content on your device before you share it. It detects secrets and PII using 30+ built-in patterns — covering everything from AWS access keys to IBANs — and replaces matches with labelled placeholders:
# Before
Authorization: Bearer eyJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJ1c2VyXzEyMzQ1In0...
aws_access_key_id = AKIAIOSFODNN7EXAMPLE
# After
Authorization: Bearer <JWT_TOKEN>
aws_access_key_id = <AWS_ACCESS_KEY>The placeholder output is safe to paste into any LLM. The original values never leave your machine.
Custom rules for your environment
Built-in patterns catch industry-standard secrets. Your environment probably has internal identifiers that also need protection — customer IDs, internal API formats, or proprietary tokens. RedactOps lets you define custom regex rules per organisation so your team-specific patterns are covered automatically.
Practical steps
- Run any log file or config snippet through RedactOps before pasting.
- Review the diff — RedactOps shows exactly what was replaced so you can verify nothing useful was removed.
- Paste the redacted output into your LLM of choice.
- For recurring workflows, add RedactOps to your team's MDM deployment so every engineer has it available without a manual install step.
The cost of a leaked API key — customer notification, credential rotation, incident investigation, potential regulatory reporting — is orders of magnitude higher than the friction of running a redaction step. Automating it removes the friction entirely.