Okay, I need to get this off my chest after spending the last two weeks deep in a ServiceNow GRC implementation. Everyone's talking about the "predictive risk scoring" and "AI-driven insights" like it's magic. But after building out the dashboards and digging into the logic... I'm convinced it's mostly sophisticated filtering with a fancy label.
Don't get me wrong, the filtering is *powerful*. But calling it "predictive" feels like a stretch. Let me explain what I mean.
In our setup, the so-called "predictive risk score" for, say, a vendor is essentially a weighted sum of attributes, many of which are static or historical. Think: `(financial_health_score * 0.3) + (compliance_incident_count * 0.25) + (data_sensitivity * 0.45)`. That's a scoring algorithm, not a prediction. A true prediction would be forecasting *future* incidents or failures based on patterns, not just repackaging existing data points with weights.
Here's a snippet of the kind of logic I'm seeing in Business Rules and Script Includes that power these features. It's clean, but it's not ML.
```javascript
// Simplified example of a 'risk score' calculation
calculatePredictiveScore: function(vendorGr) {
var score = 0;
score += vendorGr.u_financial_rating * this.weightings.financial;
score += vendorGr.u_open_issues.count * this.weightings.issues;
score += this._getRegionRiskFactor(vendorGr.u_country) * this.weightings.region;
// ... more factors
return score;
},
```
The real value, in my opinion, is in the **workflow automation** it triggers—not the "prediction." When a score passes a threshold, it can auto-create assessments, assign tasks, or escalate. *That's* fantastic. But we should call it what it is: **rule-based, weighted risk scoring.**
Am I missing something? Have any of you dug into the ML pipelines behind this? Or are your experiences similar—powerful filtering and automation, but the "predictive" label is more marketing than reality?
Would love to hear your takes, especially if you've customized these modules.
-- Weave
Prompt engineering is the new debugging
You know, that's really interesting. I've been looking at migrating some of our old on-prem risk systems to cloud platforms, and I see a lot of this same "predictive" language thrown around. It makes me nervous to promise my management something that sounds like it can see the future, when it might just be what you described.
I have to ask, because I'm trying to plan a timeline - how much of your time was spent on configuring those scoring algorithms versus trying to get actual, real forecasting models to work? Are vendors selling the first thing but calling it the second? 😬
One step at a time
Totally get that feeling about selling it to management. It's a lot of pressure.
You asked about time spent... in my last project, configuring the scoring weights took up like 80% of the effort. Setting up dashboards and reports around it took the rest. I never even *saw* a real forecast model. Is that normal?
It makes me wonder, where do you even draw the line between a "smart filter" and a true prediction?
Exactly. That's a scoring model, not a predictive one.
The line is drawn at temporal orientation. A true prediction uses past data to forecast a *future* event with a probability, like "vendor X has a 30% chance of a data breach in the next 6 months." What you described is a risk *assessment*: a snapshot of current state.
A lot of vendors slap "AI/ML" on glorified if-then rules. The weight tuning is just manual regression. If you're not training on time-series data and testing against future outcomes, it's not predictive.
Your snippet cuts off, but I bet it's just summing weighted fields.
slow pipelines make me cranky