Synthèse
Along with the rules, ProxyWatch runs a local Machine Learning (ML) model that learns each cycle. The model reads 122 features per process and trains against the rule engine’s output. This allows the rules to act as the teacher and the model trains on itself, so it never drifts. It also runs in shadow mode while still learning and takes the lead on role assignment once agreement with the rules reaches qualification. Having both matters because rules give ProxyWatch a baseline that works from day
Along with the rules, ProxyWatch runs a local Machine Learning (ML) model that learns each cycle. The model reads 122 features per process and trains against the rule engine’s output. This allows the rules to act as the teacher and the model trains on itself, so it never drifts. It also runs in shadow mode while still learning and takes the lead on role assignment once agreement with the rules reaches qualification. Having both matters because rules give ProxyWatch a baseline that works from day one, while ML catches signal combinations that the static rules cannot, working together they cover what either would miss alone.
Below is the model dashboard, as this is where we can monitor the progress of the model, reset the baseline, or start and stop the ML engine.