A.I. Frameworks and Databases | Episode 37
In Episode 37 of AI Security Ops, the team breaks down the most important AI security frameworks and vulnerability databases used to track risks in machine learning and large language models. The discussion covers emerging AI vulnerability databases, the OWASP Top 10 for LLMs, CVE challenges, and frameworks like MITRE ATLAS, highlighting why standardizing AI threats is still difficult. This episode is a practical guide for security professionals looking to stay ahead of AI vulnerabilities, attack techniques, and defensive resources in a fast-moving landscape.
Chapters
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Chapters
- (00:00) - Episode 37 – AI Frameworks & Databases
- (01:39) - A.I. vulnerability tracking is still young
- (02:44) - Should A.I. get its own vulnerability database?
- (07:33) - The benefit of multiple vulnerability databases
- (15:58) - The what is the definition of a vulnerability?
- (17:54) - Final Thoughts
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Episode Video
Creators and Guests
Host
Brian Fehrman
Brian Fehrman is a long-time BHIS Security Researcher and Consultant with extensive academic credentials and industry certifications who specializes in AI, hardware hacking, and red teaming, and outside of work is an avid Brazilian Jiu-Jitsu practitioner, big-game hunter, and home-improvement enthusiast.
Host
Bronwen Aker
Bronwen Aker is a BHIS Technical Editor who joined full-time in 2022 after years of contract work, bringing decades of web development and technical training experience to her roles in editing pentest reports, enhancing QA/QC processes, and improving public websites, and who enjoys sci-fi/fantasy, Animal Crossing, and dogs outside of work.
Host
Derek Banks
Derek is a BHIS Security Consultant, Penetration Tester, and Red Teamer with advanced degrees, industry certifications, and broad experience across forensics, incident response, monitoring, and offensive security, who enjoys learning from colleagues, helping clients improve their security, and spending his free time with family, fitness, and playing bass guitar.