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AI Workflow Engineer: Design, Build, and Operate

API Keys and Secrets: Safe Handling Without Overthinking It

Understand how to securely handle API keys and secrets within AI workflows. Learn best practices for storing keys in environment variables or secrets vaults to avoid accidental exposure. This lesson helps you implement safe management that supports efficient debugging and safe collaboration without risking credential leaks.

We'll cover the following...

A request is pasted into a ticket to show a failing call, and the screenshot still includes a key in the command:

curl https://api.example.invalid/v1/messages \
  -H "Authorization: Bearer sk-live-7f9a2c1d8e4b5a6f3c2d1e0f9a8b7c6d" \
  -H "Content-Type: application/json"
The leak: A real key pasted into a ticket, now readable by everyone with access to that ticket

curl https://api.example.invalid/v1/messages \
  -H "Authorization: Bearer $LLM_API_KEY" \
  -H "Content-Type: application/json"
The same request, safe to paste anywhere; the placeholder names where the value comes from without being the value

Now the before/after is a real difference, and the rotation sentence has something to refer to.

Even if nobody reports misuse, the key must be rotated or revoked because control over who can use it is gone the moment it leaves a private, managed place. This lesson keeps the pattern minimal so it works across tools.

Where a key should live

With the risk visible, the fix is mostly about putting the key in a storage location designed for secrets and only referencing it from requests. Three storage locations cover most beginner workflows. ...