In this episode of The MLSecOps Podcast hosted by Daryan Dehghanpisheh (Protect AI) and special guest-host Martin Stanley, CISSP (Cybersecurity and Infrastructure Security Agency), we delve into critical aspects of AI security and operations. This episode features esteemed guests, Gary Givental (IBM) and Kaleb Walton (FICO).
The group's discussion unfolds with insights into the evolving field of Machine Learning Security Operations, aka, MLSecOps. A recap of CISA's most recent Secure by Design and Secure AI initiatives sets the stage for the a dialogue that explores the parallels between MLSecOps and DevSecOps. The episode goes on to illuminate the challenges of securing AI systems, including data integrity and third-party dependencies. The conversation also travels to the socio-technical facets of AI security, explores MLSecOps and AI security posture roles within an organization, and the interplay between people, processes, and tools essential to successful MLSecOps implementation.
Thanks for listening! Find more episodes and transcripts at https://bit.ly/MLSecOpsPodcast.
Additional tools and resources to check out:
Protect AI Radar: End-to-End AI Risk Management
Protect AI’s ML Security-Focused Open Source Tools
LLM Guard - The Security Toolkit for LLM Interactions
Huntr - The World's First AI/Machine Learning Bug Bounty Platform
In this episode of The MLSecOps Podcast hosted by Daryan Dehghanpisheh (Protect AI) and special guest-host Martin Stanley, CISSP (Cybersecurity and Infrastructure Security Agency), we delve into critical aspects of AI security and operations. This episode features esteemed guests, Gary Givental (IBM) and Kaleb Walton (FICO).
The group's discussion unfolds with insights into the evolving field of Machine Learning Security Operations, aka, MLSecOps. A recap of CISA's most recent Secure by Design and Secure AI initiatives sets the stage for the a dialogue that explores the parallels between MLSecOps and DevSecOps. The episode goes on to illuminate the challenges of securing AI systems, including data integrity and third-party dependencies. The conversation also travels to the socio-technical facets of AI security, explores MLSecOps and AI security posture roles within an organization, and the interplay between people, processes, and tools essential to successful MLSecOps implementation.
Thanks for listening! Find more episodes and transcripts at https://bit.ly/MLSecOpsPodcast.
Additional tools and resources to check out:
Protect AI Radar: End-to-End AI Risk Management
Protect AI’s ML Security-Focused Open Source Tools
LLM Guard - The Security Toolkit for LLM Interactions
Huntr - The World's First AI/Machine Learning Bug Bounty Platform