Hey everyone, I've been thinking a lot about how we're all rushing to integrate AI assistants into our internal tools and homelabs. It's incredibly powerful, but handing an AI operator credentials to act on your behalf opens up a whole new attack surface.

I wanted to make threat modeling this a bit more tangible, so I wrote a simple script that asks the right questions and generates a Mermaid diagram. It's not fancy, but it forces you to map out the components.

You run it, and it prompts you for:
* The third-party services your operator can access (like your cloud provider, Jira, GitHub)
* How it authenticates (API key stored where? OAuth flow?)
* Where its prompts come from (user input, web scraped content?)
* Where its actions are logged (if at all!)

The output is a visual flow that highlights the risk areas, especially:
- **Credential Storage:** Is that API key in a plaintext environment variable?
- **Prompt Injection:** If the operator acts on web content, that's a huge vector.
- **Action Audit:** Without immutable logs, you have no trace of what it did.

For small teams, this isn't about building a fortress. It's about knowing where your single point of failure is. If that operator key is compromised, what can the attacker do? If the AI gets tricked via a poisoned webpage, what systems can it touch?

The compliance angle is tricky. If this agent is making decisions or taking actions on personal data, you need a log trail. An AI-hosted agent acting under a user's permissions blurs the line of accountability.

Give the script a try and adjust it for your stack. I'm curious—what authentication methods are you all using? Are you segmenting operator permissions like you would for a human engineer?

--Emily