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EP149 Canned Detections: From Educational Samples to Production-Ready Code

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Manage episode 384804132 series 2892548
Content provided by Anton Chuvakin. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Anton Chuvakin or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Guests:

  • John Stoner, Principal Security Strategist, Google Cloud Security

  • Dave Herrald, Head of Adopt Engineering, Google Cloud Security

Topics:

  • In your experience, past and present, what would make clients trust vendor detection content?

  • Regarding “canned”, default or “out-of-the-box” detections, how to make them more production quality and not merely educational samples to learn from?

  • What is more important, seeing the detection or being able to change it, or both?

  • If this is about seeing the detection code/content, what about ML and algorithms?

  • What about the SOC analysts who don't read the code?

  • What about “tuning” - is tuning detections a bad word now in 2023?

  • Everybody is obsessed about “false positives,” what about the false negatives? How are we supposed to eliminate them if we don’t see detection logic?

Resources:

  continue reading

197 episodes

Artwork
iconShare
 
Manage episode 384804132 series 2892548
Content provided by Anton Chuvakin. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Anton Chuvakin or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Guests:

  • John Stoner, Principal Security Strategist, Google Cloud Security

  • Dave Herrald, Head of Adopt Engineering, Google Cloud Security

Topics:

  • In your experience, past and present, what would make clients trust vendor detection content?

  • Regarding “canned”, default or “out-of-the-box” detections, how to make them more production quality and not merely educational samples to learn from?

  • What is more important, seeing the detection or being able to change it, or both?

  • If this is about seeing the detection code/content, what about ML and algorithms?

  • What about the SOC analysts who don't read the code?

  • What about “tuning” - is tuning detections a bad word now in 2023?

  • Everybody is obsessed about “false positives,” what about the false negatives? How are we supposed to eliminate them if we don’t see detection logic?

Resources:

  continue reading

197 episodes

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