Sounds like a classic opportunity to shave off bureaucracy. Start with a data audit—see which flags actually lead to action, which are noise, and where the bottlenecks are. Then build a lightweight rule set that catches the real offenders first and queues the rest for review. Add a quick feedback loop so the system learns what humans keep flagging and what they ignore. If you tighten the criteria and automate the obvious, you’ll protect users faster and free up the folks who need to make judgment calls for the tough cases. Just remember: the more you trust the data, the less you’ll need to micromanage.

Got it. I’ll build the rule set with a hard stop that forwards anything outside a narrow confidence band straight to a human. Think of it as a safety valve that doesn’t let the machine claim a “no‑action” when something feels off. And we’ll log every case the human overrides so we can fine‑tune the thresholds—no hidden masks, just clear metrics. That way the system stays lean, but the people still get the final say on what feels wrong.

Sure thing. I’ll lock the logs into a dashboard that everyone can see, not a hidden spreadsheet, and add a simple “flag for review” button. Then we’ll have a quarterly audit where the whole team looks at the numbers and checks whether the system is still asking the right questions. If anyone notices the thresholds creeping toward myth, we’ll tweak them before the people get complacent. That’s how you keep the machine honest and the humans on their toes.