The article discusses the role of documentation in detection engineering within security operations centers (SOCs). It compares the effectiveness of documentation created by detection engineers to that generated by large language models (LLMs). The findings suggest that while LLMs can produce actionable documentation, human-generated documentation tends to be more specific and tailored to particular environments.
Keypoints :
- Generative AI can write about various topics, raising questions about the need for human documentation in detection engineering.
- Documentation is crucial for SOC analysts to understand detection rules, investigate alerts, and respond appropriately.
- The effectiveness of AI SOC analysts may depend on the quality and specificity of documentation provided by detection engineers.
- Automated SOC workflows involve alert generation, normalization, enrichment, and evaluation of alerts for true or false positives.
- AI-powered SOCs aim to enhance the automation of investigative steps and responses compared to traditional SOAR applications.
- LLMs can produce both generic and specific investigative steps, but human-generated steps tend to be more detailed.
- The analysis revealed that LLMs achieved an overlap of about 58.37% with human-generated investigative steps.
- Human documentation allows for greater specificity and tailoring to unique organizational environments, which LLMs may lack.
- The study utilized Elastic’s documented rules to evaluate LLM effectiveness, with statistics suggesting reasonable baseline details from AI.
- Future analysis could involve comparing human and LLM-generated documentation from a wider range of detection repositories.
Full Story: https://detect.fyi/can-we-stop-documenting-our-detections-ded2201ec09b?source=rss—-d5fd8f494f6a—4