Continuous Monitoring, Explained Like a Smoke Detector

A one-time audit is a photograph. Continuous monitoring is a video feed. You need both, but only one of them catches the fire while it's still small.

Think of a smoke detector. You don't inspect your house for fire once a year and call it done. The detector watches constantly, and it bothers you the moment something looks wrong. That's what monitoring does for AI: it watches the system's everyday behavior and raises a hand when the pattern shifts.

What should you watch? Four things cover most of it: the outputs (are decisions still sensible?), the error rate (is it climbing?), the data (has the input changed underneath you?), and the humans (are complaints or overrides increasing?).

Then decide in advance what "wrong" looks like. A threshold, a trigger, a named person who gets the alert. Monitoring without a response plan is just expensive worrying.

Set it up, write down what you're watching and why, and review the setup quarterly. The audit will still happen once a year. But between audits, the smoke detector is on duty — and that's what actually keeps people safe.

I explain frameworks; I don't give legal counsel.