Guest: Dr Gary McGraw, founder of the Berryville Institute of Machine Learning Topics: Gary, you’ve been doing software security for many decades, so tell us: are we really behind on securing ML and AI systems?  If not SBOM for data or “DBOM”, then what? Can data supply chain tools or just better data governance practices help? How would you threat model a system with ML in it or a new ML system you are building?  What are the key differences and similarities between securing AI and securing a traditional, complex enterprise system? What are the key differences between securing the AI you built and AI you buy or subscribe to? Which security tools and frameworks will solve all of these problems for us?  Resources: EP135 AI and Security: The Good, the Bad, and the Magical Gary McGraw books “An Architectural Risk Analysis Of Machine Learning Systems: Toward More Secure Machine Learning“ paper “What to think about when you’re thinking about securing AI” Annotated ML Security bibliography   Tay bot story (2016) “Can you melt eggs?” “Microsoft AI researchers accidentally leak 38TB of company data” “Random number generator attack” “Google's AI Red Team: the ethical hackers making AI safer” Introducing Google’s Secure AI Framework