In this episode of the MLSecOps Podcast, we delve into the critical world of security for AI and machine learning with our guest Ron F. Del Rosario, Chief Security Architect and AI/ML Security Lead at SAP ISBN. The discussion highlights the contextual knowledge gap between ML practitioners and cybersecurity professionals, emphasizing the importance of cross-collaboration and foundational security practices. We explore the contrasts of security for AI to that for traditional software, along with the risk profiles of first-party vs. third-party ML models. Ron sheds light on the significance of understanding your AI system's provenance, having necessary controls, and audit trails for robust security. He also discusses the "Secure AI/ML Development Framework" initiative that he launched internally within his organization, featuring a lean security checklist to streamline processes. We hope you enjoy this thoughtful conversation!
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
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