Manage Authentication, Authorization, and RBAC for AI workloads on Azure: Review

We needed to sort out the IAM policies for a new AI project, and this path came up. It’s supposed to be about an hour, but it’s really just three separate product documents stapled together. One for Foundry, one for Azure AI services, and one for Azure ML. They all talk about authentication, but that's the only link. There’s no build-up, no running project, just a sudden context switch from one product page to the next.

The content itself is a quick tour of what's possible with Entra ID and built-in RBAC roles. If you've worked on Azure for any real length of time, you already know 90% of this. The other 10% is just attaching that knowledge to the names 'Azure OpenAI' or 'Azure Machine Learning Studio'. The path tells you *that* you can create custom roles or use managed identities. It doesn't show you how. No code, no CLI commands, no portal screenshots of where the buttons are.

I was hoping for a concrete scenario. Something like: here's an app that needs to call Azure OpenAI, and here's a service that trains a model—how do you lock it all down? What does the token flow look like? What are the common footguns? Instead, I got a PowerPoint presentation in text form. It functions as a quick reference for the names of services and roles, but to actually build something secure, you’re going right back to the proper technical documentation anyway. This path just points you to the table of contents.