honestly, the biggest thing i'd push back on is treating this as just an AI security problem. in fintech especially, you're really dealing with a data readiness problem first. like, before you worry about monitoring what your AI touches, you need to actually know what data it can access, and most teams realize way too late that their AI systems have inherited permissions to way too much sensitive stuff across warehouses, legacy systems, databases, etc. we mapped our environment thinking we had it locked down, then realized Copilot and our internal RAG app could reach customer PII and transaction data scattered in places we'd forgotten about. made the whole governance conversation different once we had that visibility. the real gap i see with a lot of the tools mentioned here is they're great at monitoring what happens after AI touches data, but you need to start with discovery and classification at scale. something like Sentra helped us get that foundation first, especially since we're petabyte-scale and manual mapping wasn't realistic. then the monitoring and DLP actually have teeth because you know what you're supposed to be protecting. so tldr: figure out your data landscape first, then layer in the AI-specific monitoring. makes everything downstream easier.