We were testing an AI-based tool for processing user documents, and someone flagged that it was storing data longer than expected. That opened up a whole privacy can of worms. Now I'm trying to figure out how to handle data privacy properly when working with AI features. What are the best practices you've seen?

That kind of situation can spiral fast if it’s not caught early. A while ago, we built a chatbot for internal HR queries, and even though it wasn't public-facing, we had to rethink how we managed sensitive inputs like salaries or ID numbers. We ended up implementing data masking and set strict retention policies from the start. If you're looking for ideas, https://agileengine.com/ai-studio/ has a section that walks through how to handle privacy and security in AI apps. That helped us set up boundaries early in the design process instead of patching things later. Worth checking out for frameworks and real examples.

I’m mostly on the infrastructure side, but this is something we’ve been hearing a lot about from legal and compliance teams. There’s a growing push to make privacy not just a technical feature but a design principle from the start. Even for internal tools, people expect transparency — who sees their data, how long it’s stored, what it’s used for. Feels like balancing AI innovation and privacy expectations is becoming just as important as model performance.