Integrating Large Language Models into Security Incident Response
summary
Study evaluates LLMs on 50 real incidents with 18 security analysts and finds LLMs omit critical details in 35% of cases and inject factual errors in 42%, so autonomous summarization fails; human summaries win 61% in direct comparisons. Using an LLM (Gemini pre‑2.0) as a draft reduces analyst effort, improves readability and consistency, and AI‑assisted summaries beat originals in 77% of blind comparisons, but factuality and specific naming remain failure modes.
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chapters (7)
- 0:00LLMs in security response
- 1:13LLM summary accuracy
- 3:59Human vs AI summaries
- 6:06AI summaries feedback
- 8:51AI vs human summaries
- 10:17Key takeaways and future
- 12:25Q&A
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