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AI/ML Security in Retrospect: Insights from Season 1 of The MLSecOps Podcast (Part 1)

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Manage episode 377528844 series 3461851
Innehåll tillhandahållet av MLSecOps.com. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av MLSecOps.com eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

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*This episode is also available in video format! Click to watch the full YouTube video.*
Welcome to the final episode of the first season of The MLSecOps Podcast, brought to you by the team at Protect AI.

In this two-part episode, we’ll be taking a look back at some favorite highlights from the season where we dove deep into machine learning security operations. In this first part, we’ll be revisiting clips related to things like adversarial machine learning; how malicious actors can use AI to fool machine learning systems into making incorrect decisions; supply chain vulnerabilities; and red teaming for AI/ML, including how security professionals might simulate attacks on their own systems to detect and mitigate vulnerabilities.
If you’re new to the show, or if you could use a refresher on any of these topics, this episode is for you, as it’s a great place for listeners to start their learning journey with us and work backwards based on individual interests. And when something in this recap piques your interest, be sure to check out the transcript for links to the full-length episodes where each of these clips came from. You can visit the website and read the transcripts at www.mlsecops.com/podcast.
So now, we invite you to sit back, relax, and enjoy this Season 1 recap of some of the most important MLSecOps topics of the year. And stay tuned for part 2 of this episode, where we’ll be revisiting MLSecOps conversations surrounding governance, risk, and compliance, model provenance, and Trusted AI. Thanks for listening.
Chapters:
0:00 Opening
0:25 Intro by Charlie McCarthy, MLSecOps Community Leader
2:15 S1E1 with Guest Disesdi Susanna Cox
5:08 S1E2 with Guest Dr. Florian Tramèr
10:16 S1E3 with Guest Pin-Yu Chen, PhD
13:18 S1E5 with Guest Christina Liaghati, PhD
17:59 S1E6 with Guest Johann Rehberger
22:10 S1E10 with Guest Kai Greshake
27:14 S1E11 with Guest Shreya Rajpal
31:45 S1E12 with Guest Apostol Vassilev
36:36 End/Credits
Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models

Recon: Automated Red Teaming for GenAI

Protect AI’s ML Security-Focused Open Source Tools

LLM Guard Open Source Security Toolkit for LLM Interactions

Huntr - The World's First AI/Machine Learning Bug Bounty Platform

  continue reading

41 episoder

Artwork
iconDela
 
Manage episode 377528844 series 3461851
Innehåll tillhandahållet av MLSecOps.com. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av MLSecOps.com eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Send us a text

*This episode is also available in video format! Click to watch the full YouTube video.*
Welcome to the final episode of the first season of The MLSecOps Podcast, brought to you by the team at Protect AI.

In this two-part episode, we’ll be taking a look back at some favorite highlights from the season where we dove deep into machine learning security operations. In this first part, we’ll be revisiting clips related to things like adversarial machine learning; how malicious actors can use AI to fool machine learning systems into making incorrect decisions; supply chain vulnerabilities; and red teaming for AI/ML, including how security professionals might simulate attacks on their own systems to detect and mitigate vulnerabilities.
If you’re new to the show, or if you could use a refresher on any of these topics, this episode is for you, as it’s a great place for listeners to start their learning journey with us and work backwards based on individual interests. And when something in this recap piques your interest, be sure to check out the transcript for links to the full-length episodes where each of these clips came from. You can visit the website and read the transcripts at www.mlsecops.com/podcast.
So now, we invite you to sit back, relax, and enjoy this Season 1 recap of some of the most important MLSecOps topics of the year. And stay tuned for part 2 of this episode, where we’ll be revisiting MLSecOps conversations surrounding governance, risk, and compliance, model provenance, and Trusted AI. Thanks for listening.
Chapters:
0:00 Opening
0:25 Intro by Charlie McCarthy, MLSecOps Community Leader
2:15 S1E1 with Guest Disesdi Susanna Cox
5:08 S1E2 with Guest Dr. Florian Tramèr
10:16 S1E3 with Guest Pin-Yu Chen, PhD
13:18 S1E5 with Guest Christina Liaghati, PhD
17:59 S1E6 with Guest Johann Rehberger
22:10 S1E10 with Guest Kai Greshake
27:14 S1E11 with Guest Shreya Rajpal
31:45 S1E12 with Guest Apostol Vassilev
36:36 End/Credits
Thanks for checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models

Recon: Automated Red Teaming for GenAI

Protect AI’s ML Security-Focused Open Source Tools

LLM Guard Open Source Security Toolkit for LLM Interactions

Huntr - The World's First AI/Machine Learning Bug Bounty Platform

  continue reading

41 episoder

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