A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications
Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - especially in Security Operations Centres (SOCs), where analysts face high workload, burnout and confidentiality constraints - is difficult and often results in small samples. Large language models (LLMs) oer an appealing alternative by generating synthetic responses at scale, but little guidance exists on when such surrogate participants are reliable. We present a methodological framework for evaluating LLMs as substitutes or supplements to expert survey respondents. Using responses from SOC professionals, we compare persona-based and aggregate LLM-genera...
| Authors | Despoina Giarimpampa, Roland Meier, Tegawendé F. Bissyandé et al. |
|---|---|
| Primary category | cs.CY |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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