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Risk profiles turn raw evaluation scores into one risk assessment per model. A risk category is a named risk dimension backed by evaluators scoped to datasets, and a profile is a weighted set of categories tuned for a use case. Scoring a profile against a model produces an overall risk score and risk level.

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Before you start

You need:
  • At least one risk category to weigh.
  • Evaluation results for that category’s evaluators, produced by running them against datasets, usually through an examination.
A category’s score comes from stored evaluation results, so a profile built on categories that have never been evaluated produces nothing to score.

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Create a risk profile

1

Create New Profile

On the Risk Profiles page, click Create New Profile. To change an existing profile, click its card.
2

Describe the profile

In the Profile panel, set the Name, Description, Use Case, Domain, and Method. Domain offers government, healthcare, finance, and education. The Active toggle controls whether the profile is available for scoring.
3

Add categories and weights

Drag categories from Categories onto the Profile Canvas. Each card shows that category’s aggregation method and evaluator count, and takes a Weight as a percentage. Auto-Balance distributes the weights evenly, and Table View shows the same categories as a grid.
4

Save Changes

Click Save Changes.
On the Risk Profiles page, profiles appear as cards showing the use case, domain, category count, a weight-distribution bar, tags, and the date modified. Profiles SeekrGuard provides carry a System badge. Each card’s menu offers Edit and Duplicate. Delete appears only on profiles you created. Above the cards, search by name, narrow the list with Filter, and filter by Visibility.

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Understand how a risk score is calculated

When a profile is scored against a model, SeekrGuard works bottom-up:
  1. Normalize each evaluator. Each evaluation’s score is scaled to a 0 to 1 range, (score - min) / (max - min). For evaluators whose polarity is not positive, the value is inverted, so that higher means better at this stage.
  2. Score each category. A category combines its evaluator and dataset pairings using its aggregation method.
  3. Aggregate to an overall score. The profile’s method combines the category scores by weight, then the result is inverted so that a higher score means more risk.
A category with no evaluation results is left out of the calculation rather than counted as zero. A model assessed on two of a profile’s five categories is scored on those two, so scores are comparable only between models with the same coverage. The overall score appears on a 0 to 100 scale and maps to a risk level.
On the risk pages a higher score means more risk. The composite on a model card measures the same evaluations in reverse, where higher is better and 80 or above reads strong.

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Read model risk scores

Per-model risk scores appear in the Model Risk Assessment section of the Home page, where you select the profile to score against, and on each model card, where you pick a profile in its scorecard. The model card breaks the score down by category, dataset, and evaluator under its Risk Performance, Compare, and Explore tabs.
Risk scores come from stored evaluation results. If a profile’s categories have no evaluations behind them, there is nothing to score. Run the evaluators against their datasets first, usually through an examination.
Last modified on August 17, 2026