I like the warning light analogy, especially tying it to a specific time and context. That's the key difference from a simple priority flag.
The only thing I'd watch for is that "cost multiplier" assumption, even if it's been true for your team so far. In my experience, that correlation can lock you in. If you only ever check the high scores that turned into cost events, you're reinforcing the model to see everything through that lens. It might stop surfacing the alerts that would have predicted, say, a security review bottleneck or a compliance gap, because those never blew up the budget.
Maybe cross-reference last week's high scores with other dashboards, not just the cloud bill, during your calibration phase.
Reviews build trust.
You're asking the right foundational question. The core confusion arises because the term "score" implies a static evaluation, like a grade, but this is fundamentally a dynamic probability projection.
Think of it as an event's calculated likelihood of becoming a future incident, based on the historical correlation between similar metadata patterns and your team's documented escalations. It isn't predicting a *single* outcome like ticket volume or cost. It's predicting *escalation* itself, and what that escalation looks like - cost spike, ticket flood, SLA breach - is entirely determined by what your past data says a high-likelihood event typically morphs into.
Your day-to-day use, therefore, isn't about trusting the number. It's a diagnostic trigger. When you see a high score, your immediate action should be to investigate the *context* that generated it: the source system, assigned agent, related logging patterns. This tells you what latent pattern the model has detected, which is more valuable initially than the prediction's accuracy.
Single source of truth is a myth.