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Practical Foundations for Securing AI

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Manage episode 418235989 series 3461851
内容由MLSecOps.com提供。所有播客内容(包括剧集、图形和播客描述)均由 MLSecOps.com 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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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 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

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

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Manage episode 418235989 series 3461851
内容由MLSecOps.com提供。所有播客内容(包括剧集、图形和播客描述)均由 MLSecOps.com 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Send us a text

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 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

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

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