AI Security: Map It, Manage It, Master It

03/13/25 • 41 min

Full transcript with links to resources available at https://mlsecops.com/podcast/ai-security-map-it-manage-it-master-it

In part one of our two-part MLSecOps Podcast episode, security veteran Brian Pendleton takes us from his early hacker days to the forefront of AI security. Brian explains why mapping every AI integration is essential for uncovering vulnerabilities. He also dives into the benefits of using SBOMs over model cards for risk management and stresses the need to bridge the gap between ML and security teams to protect your enterprise AI ecosystem.

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Full transcript with links to resources available at https://mlsecops.com/podcast/ai-security-map-it-manage-it-master-it

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Previous Episode

Agentic AI: Tackling Data, Security, and Compliance Risks

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Full transcript with links to resources available at https://mlsecops.com/podcast/agentic-ai-tackling-data-security-and-compliance-risks

Join host Diana Kelley and CTO Dr. Gina Guillaume-Joseph as they explore how agentic AI, robust data practices, and zero trust principles drive secure, real-time video analytics at Camio. They discuss why clean data is essential, how continuous model validation can thwart adversarial threats, and the critical balance between autonomous AI and human oversight. Dive into the world of multimodal modeling, ethical safeguards, and what it takes to ensure AI remains both innovative and risk-aware.

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Next Episode

Rethinking AI Red Teaming: Lessons in Zero Trust and Model Protection

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Full transcript with links to resources available at https://mlsecops.com/podcast/rethinking-ai-red-teaming-lessons-in-zero-trust-and-model-protection

This episode is a follow up to Part 1 of our conversation with returning guest Brian Pendleton, as he challenges the way we think about red teaming and security for AI. Continuing from last week’s exploration of enterprise AI adoption and high-level security considerations, the conversation now shifts to how red teaming, zero trust, and privacy concerns intertwine with AI’s unique risks.

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