A risk practitioner is estimating the likelihood of a ransomware event affecting a manufacturing firm's operational technology environment. Historical incident data is sparse, so the practitioner convenes plant engineers, security staff, and the insurance broker to elicit calibrated estimates and combine them into a reasoned likelihood. Which technique is being used?
The Delphi technique gathers anonymous, iterative expert judgments and converges them toward a calibrated consensus, which fits sparse-data situations perfectly. By combining engineers, security, and the broker, the practitioner draws on operational, technical, and actuarial perspectives. Structured elicitation reduces anchoring and groupthink, producing a reasoned likelihood estimate where historical incident data alone cannot support one.
Why this answer
When incident data is too sparse to support statistical estimation, structured expert elicitation such as the Delphi technique is the appropriate way to generate calibrated likelihood estimates. It pools diverse expertise, uses anonymity and iteration to reduce bias, and converges on a defensible consensus. The resulting estimate can later feed quantitative models if data improves, keeping the analysis honest about its uncertainty.
Exam trap
The trap here is reaching for a quantitative model like Monte Carlo or Bayesian updating when the real problem is that no data exists yet to parameterize those models.