REVIEW 1 cited by
Using LLMs for Tabletop Exercises within the Security Domain
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Using LLMs for Tabletop Exercises within the Security Domain
read the original abstract
Tabletop exercises are a crucial component of many company's strategy to test and evaluate its preparedness for security incidents in a realistic way. Traditionally led by external firms specializing in cybersecurity, these exercises can be costly, time-consuming, and may not always align precisely with the client's specific needs. Large Language Models (LLMs) like ChatGPT offer a compelling alternative. They enable faster iteration, provide rich and adaptable simulations, and offer infinite patience in handling feedback and recommendations. This approach can enhances the efficiency and relevance of security preparedness exercises.
Forward citations
Cited by 1 Pith paper
-
Assessment in Team Problem-Solving Exercises in Computing Education
Clustering teams by their logged actions in cybersecurity tabletop exercises aligns reasonably with instructor scores, while GPT-4o and GPT-5.2 rubric-based assessments of team communication still deviate substantiall...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.