Using LLMs for Tabletop Exercises within the Security Domain
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909300635467776 |
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| author | Hays, Sam White, Jules |
| author_facet | Hays, Sam White, Jules |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using LLMs for Tabletop Exercises within the Security Domain Hays, Sam White, Jules Cryptography and Security 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. |
| title | Using LLMs for Tabletop Exercises within the Security Domain |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2403.01626 |