Severity

Every finding carries a severity - critical, high, medium, low, or info. Severity is derived from the finding’s score alone: the score falls into one of five bands, and each band maps to one severity. The same bands apply to every goal category - a credential_leak and a content_policy finding with the same score get the same severity.

How severity is derived

Severity	Score
critical	>= 0.9
high	0.7 - 0.9
medium	0.5 - 0.7
low	0.3 - 0.5
info	< 0.3

One label per band, no per-category weighting - so there is nothing to memorize per category. If you want to weight a category (e.g. treat a credential_leak as critical from a lower score), lower the cutoffs with a per-assessment policy rather than special-casing the category.

Note

Severity reflects coverage and triage priority, not certification. See Compliance for how findings map to OWASP, MITRE ATLAS, NIST AI RMF, and Google SAIF.

Bring your own severity policy

Different organizations have different risk appetites. You can attach a severity policy to an assessment to adjust the score bands (thresholds) or, for advanced use, weight specific categories with your own matrix and default_row.

The policy is part of the assessment request, so it travels with the run and is applied consistently to every finding in that assessment.

Fields

Field	Type	Description
thresholds	float[5]	Descending score cutoffs for the five bands. Defaults to [0.9, 0.7, 0.5, 0.3, 0.0].
matrix	{category: string[5]}	Advanced: per-category severity labels aligned to the bands. Omit for score-only severity.
aliases	{category: category}	Advanced: reuse another category’s matrix row for a given category.
default_row	string[5]	Severity labels for categories not in matrix. Defaults to critical, high, medium, low, info.

Most policies only need thresholds. Each matrix row is exactly five labels from critical, high, medium, low, info, ordered highest-band first. Keep one label per band; to make a category critical from a lower score, lower the cutoff with thresholds rather than repeating a label in the row.

Example: extend the defaults

Promote radicalization and make it critical from a lower score by lowering the critical cutoff to 0.7:

{
  "name": "Policy-aligned run",
  "target_model": "dn/claude-opus-5-5",
  "severity_policy": {
    "thresholds": [0.7, 0.5, 0.3, 0.1, 0.0],
    "matrix": {
      "radicalization": ["critical", "high", "medium", "low", "info"]
    }
  }
}

The row keeps one label per band (no repeats); lowering the cutoffs is what makes a 0.7 score land in the top (critical) band.

Example: advanced per-category weighting

Weight specific categories with your own matrix, and set a default_row for everything else:

{
  "name": "Internal risk taxonomy",
  "target_model": "dn/gpt-6-sol",
  "severity_policy": {
    "thresholds": [0.9, 0.75, 0.5, 0.25, 0.0],
    "matrix": {
      "pii_extraction": ["critical", "high", "medium", "low", "info"],
      "brand_safety": ["high", "medium", "low", "low", "info"]
    },
    "default_row": ["medium", "low", "low", "info", "info"]
  }
}

Any category not in matrix uses default_row, so the classification is entirely yours.

Tip

Omit severity_policy for the default score-only classification. An empty policy is a no-op, and an invalid policy is rejected at request time, so a run never scores against a malformed mapping.