ML Risk Scoring and Manual Filters: Designing the Boundary

Use manual filters for requirements that must be explicit, such as an allowed country set or a test IP range. Use risk scoring when several weak signals need a combined assessment and no single attribute justifies the decision alone.

Decide how an allowlist, deny rule, score threshold, and fallback interact. Write examples at boundary values and conflicting conditions, because an undocumented override order can make a correct individual rule produce an unexpected final route.

Collect decisions without changing destinations first, then review score distribution and reasons by source. Move to active routing only after known legitimate test sessions and deliberately suspicious fixtures behave consistently across several controlled runs.

A false positive keeps an intended visitor from the Target Page; a false negative sends questionable traffic onward. Lowering one error type can raise the other, so sensitivity should be tuned for a named flow rather than copied blindly between sources.

Record who changed a rule or threshold, why it changed, and which event sample supported the decision. Verification means replaying the acceptance cases after every adjustment and confirming that manual exceptions still behave as documented.