In this episode, I sit down with <a href='https://www.linkedin.com/in/ACoAAAL17YQBd8TYz7_s80r90yAuFNZiXxMhOBs'>Daniel Bardenstein</a>, CTO & Co-Founder of <a href='https://www.manifestcyber.com/'>Manifest Cyber</a>.We discussed the AI supply chain security, including open source risks, AIBOMs, best practices for CISOs, and regulatory approaches in the U.S. and EU.We dove into:<ul><li>What is the same and different between the risks AI introduces across the enterprise compared to open source software, and where and how the two converge.</li><li>The rise of an “<a href='https://github.com/manifest-cyber/aibom'>AIBOM</a>” and why it is becoming a critical part of enterprise risk management in the AI Era</li><li>The work Daniel and others are doing as part of a Tiger Team defining “<a href='https://github.com/aibom-squad/SBOM-for-AI-Tiger-Team/tree/main/SBOM-for-AI-Use-Cases'>SBOM-for-AI-Use Cases</a>”.</li><li>Why is it so difficult for organizations to gain visibility into their AI models' internals, especially training data, model provenance, and pipeline dependencies?</li><li>Where CISOs and security teams can get started when it comes to understanding where and how AI is being used and avoiding some mistakes.</li><li>Gaps among the current waves of AI security startups and how they contrast with the approach Manifest is taking when managing AI supply chain risks.</li><li>Real-world insights and examples of how organizations operationalize SBOM for risk reduction.</li><li>Key differences between the U.S. and EU regarding regulatory approaches to AI and supply chain security risks.</li></ul>

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Resilient Cyber w/ Daniel Bardenstein - AI Supply Chain Security Risks

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