Threat-model
Define decisions, affected populations, failure modes, policy constraints, and release thresholds.
AI assurance
Test model behaviour beyond the data you happened to collect.
Build targeted populations, boundary cases, and distribution shifts to examine robustness, fairness, and policy performance.
02.3
Example constraint
A high-impact model performs well on a broad holdout set but has too little evidence for rare groups, boundary conditions, and plausible future shifts.
Ready for qualification
A concrete foundry workflow
A high-impact model performs well on a broad holdout set but has too little evidence for rare groups, boundary conditions, and plausible future shifts.
Simulate
Generate controlled cohorts, perturbations, rare events, and distribution shifts with reproducible seeds.
Measure
Compare model behaviour by scenario, group, severity, confidence, calibration, and policy outcome.
Decide
Route exceptions and residual risk to the named model owner with the evidence attached.
Acceptance measureFailure-mode coverage and pass rate
Baseline → threshold → observed result
Controls to scope
✓Scenario validity and provenance
✓Fairness and robustness thresholds
✓Model-version traceability
✓Named validation owner
Start with one release
Qualify the first ai assurance release.
Start with one restricted dataset, one model decision, and explicit acceptance criteria.
Request an assessment