Sure, a lightweight approach would be to build a streaming logistic regression that feeds on the current raid metrics—like packet loss, latency spikes, CPU load, and connection counts. As each new datapoint comes in, the model updates the probability of a breach in real time. You can calibrate the threshold to flag alerts when the probability crosses, say, 0.8. If you want something even faster, a rule‑based system that watches for sudden spikes in failed login attempts or unauthorized API calls can serve as a quick heuristic until the model kicks in. Just make sure to log every feature value so you can retrain later when you have more data.

Got it, I’ll limit the feature set to the most predictive metrics, set a hard threshold for the breach probability, and make the alert trigger deterministic. All raw data will be logged for later retraining, so the ops team can see exactly why the alert fired.

Sounds good. I'll keep it tight, log everything, and let the data guide the response. Let me know if anything changes.