I am currently building something similar for my company. We are enabling people to host apps with python backends as well, so that people can do more complex tweaking of data for scientific workflows. The out-of-box solutions keep users constrained to an environment that doesn't allow things like importing weird packages on the back and front end for custom apps. The security implications are challenging, but I can mostly just keep the apps hosted in a highly constrained environment.

Separately I haven't yet seen a great security governance model for LLM integrations. At an enterprise level I'd like to govern rules such that, for example, if someone gives an LLM access PII information or proprietary data, then it shouldn't have access to a slack integration or the internet. Controlling this at the employee or team level doesn't make sense, as an employee may have reason to make separate use of both. From a security perspective I want them to be able use LLMs with different types of acccess, but not necessarily the same agent at the same time. Furthermore, they could ideally chain together agents with different permissions in specific orders. For example they could have a workflow where an agent can reach out to the internet, and then have a separate one that can read/write to slack, and then have a third that can interact with PII data. If they tried to wire together agents in the opposite order, it should get denied.

Thanks! That's the best kind of validation. If you ever take Prized for a spin, would genuinely love to hear how it compares to what you built, especially on the data access side.