Hello All,I am building a tool for detecting shadow AI (or Embedded AI). My process involves ingesting logs and classifying them as either shadow AI or not, then returning a CSV.I want to improve it more and am looking for some input on what else I can add to the dashboard?What would be meaningful as a data point aside from just identifying whether this tool is Shadow/Embedded AI or not.Would...
While we call LLMs(internal and external, based on instruction type), the output generated by LLMs can't be taken as ground truths unless we do rigorous evaluations. We have our own metrics when it comes to what could be called a ground truth, based on the user's seed information and business logic. Accuracy & preciseness needs also differ from use-case to use case. Function calling adds in another layer.
Another value add is type of instructions that we can generate. We expose 7 currently, and are working on exposing more instruction types. The challenge is to create ground truth of wide variety of cases that a given user can ask for a business including guardrailing.
We have built internal tools and agents to solve for those, and are internally discussing the ideal way to expose it to users, and whether it would be beneficial for the community. Any thoughts on that would be appreciated.
Automation took a significant amount of time for us as well, so at scale, even a reliable automated CI/CD pipeline is indeed a value add in itself.
Lmk if I can add more details to answer the question.