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Anton Chuvakin에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Anton Chuvakin 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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EP241 From Black Box to Building Blocks: More Modern Detection Engineering Lessons from Google

31:33
 
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Manage episode 503751053 series 2892548
Anton Chuvakin에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Anton Chuvakin 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Guest:

Topics:

  • On the 3rd anniversary of Curated Detections, you've grown from 70 rules to over 4700. Can you walk us through that journey? What were some of the key inflection points and what have been the biggest lessons learned in scaling a detection portfolio so massively?
  • Historically the SecOps Curated Detection content was opaque, which led to, understandably, a bit of customer friction. We’ve recently made nearly all of that content transparent and editable by users. What were the challenges in that transition?
  • You make a distinction between "Detection-as-Code" and a more mature "Software Engineering" paradigm. What gets better for a security team when they move beyond just version control and a CI/CD pipeline and start incorporating things like unit testing, readability reviews, and performance testing for their detections?
  • The idea of a "Goldilocks Zone" for detections is intriguing – not too many, not too few. How do you find that balance, and what are the metrics that matter when measuring the effectiveness of a detection program? You mentioned customer feedback is important, but a confusion matrix isn't possible, why is that?
  • You talk about enabling customers to use your "building blocks" to create their own detections. Can you give us a practical example of how a customer might use a building block for something like detecting VPN and Tor traffic to augment their security?
  • You have started using LLMs for reviewing the explainability of human-generated metadata. Can you expand on that? What have you found are the ripe areas for AI in detection engineering, and can you share any anecdotes of where AI has succeeded and where it has failed?

Resources

  continue reading

248 에피소드

Artwork
icon공유
 
Manage episode 503751053 series 2892548
Anton Chuvakin에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Anton Chuvakin 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Guest:

Topics:

  • On the 3rd anniversary of Curated Detections, you've grown from 70 rules to over 4700. Can you walk us through that journey? What were some of the key inflection points and what have been the biggest lessons learned in scaling a detection portfolio so massively?
  • Historically the SecOps Curated Detection content was opaque, which led to, understandably, a bit of customer friction. We’ve recently made nearly all of that content transparent and editable by users. What were the challenges in that transition?
  • You make a distinction between "Detection-as-Code" and a more mature "Software Engineering" paradigm. What gets better for a security team when they move beyond just version control and a CI/CD pipeline and start incorporating things like unit testing, readability reviews, and performance testing for their detections?
  • The idea of a "Goldilocks Zone" for detections is intriguing – not too many, not too few. How do you find that balance, and what are the metrics that matter when measuring the effectiveness of a detection program? You mentioned customer feedback is important, but a confusion matrix isn't possible, why is that?
  • You talk about enabling customers to use your "building blocks" to create their own detections. Can you give us a practical example of how a customer might use a building block for something like detecting VPN and Tor traffic to augment their security?
  • You have started using LLMs for reviewing the explainability of human-generated metadata. Can you expand on that? What have you found are the ripe areas for AI in detection engineering, and can you share any anecdotes of where AI has succeeded and where it has failed?

Resources

  continue reading

248 에피소드

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