The article, co-written with Maciej Mensfeld from Mend.io, discusses how Mend.io used Anthropic Claude on Amazon Bedrock to analyze and categorize over 70,000 Common Vulnerabilities and Exposures (CVEs). By leveraging large language models (LLMs), Mend.io automated the process of identifying CVEs with specific attack requirement details, significantly reducing the time required from 200 days to a much shorter period. This automation allowed them to provide higher quality and more actionable insights to their customers, giving them a competitive edge.
The article highlights the challenges faced during the integration of LLMs, such as managing quota limitations, estimating costs, and handling unexpected model responses. It also covers the process of selecting the right model, crafting effective prompts, and analyzing the results. The unique capabilities of Anthropic Claude, especially its ability to recognize XML tags within prompts, made it particularly suitable for Mend.io’s needs.
The use of Anthropic Claude enabled Mend.io to extract critical attack requirement details from unstructured CVE reports, a task that is vital for assessing and mitigating potential risks but is impractical to perform manually due to the sheer volume of data. The article emphasizes the importance of prompt refinement and detailed context in achieving precise and valuable AI analysis.
Despite encountering challenges such as higher-than-expected costs and the need to fine-tune prompts for better alignment with desired output formats, Mend.io successfully identified CVEs with attack requirement details. This success underscores the potential of generative AI in cybersecurity, not just for vulnerability analysis but also for threat detection, incident response, and beyond.
The article concludes by encouraging cybersecurity professionals to explore the capabilities of Amazon Bedrock and Anthropic Claude models to enhance their security operations and stay ahead of evolving cyber threats.
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