Resilient Cyber w/ Helen Oakley - Exploring the AI Supply Chain
Oct 8, 2024
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Helen Oakley, an expert in software supply chain security at SAP, discusses the complexities of securing AI supply chains in a rapidly evolving landscape. She highlights the need for transparency and risk assessment to mitigate vulnerabilities. Oakley introduces the concept of AI-BOMs, which provide critical insights into AI models and datasets, and contrasts them with traditional SBOMs. The conversation also touches on the implications of AI regulations in the U.S. and EU, underscoring compliance challenges in high-stakes sectors like healthcare and finance.
The shift from conventional software to AI necessitates continuous monitoring of dynamic components, highlighting the need for AI-specific Software Bill of Materials (SBOM) adjustments.
Organizations must develop a robust AI governance framework to evaluate risks and ensure compliance, especially in high-stakes sectors like healthcare and critical infrastructure.
Deep dives
The Evolution of Software Supply Chain Security in AI
Software supply chain security has adapted with the introduction of artificial intelligence, as traditional models now face new complexities. Unlike conventional software, where the components remain static until a new build, AI's dynamic nature requires continuous monitoring of changes and dependencies throughout its runtime. This shift necessitates an expansion of the standard Software Bill of Materials (SBOM) to accommodate AI-specific elements, such as information about models and training datasets. The challenge lies in understanding these real-time alterations and the associated risks that may arise, highlighting the need for a comprehensive approach to AI supply chain transparency.
Navigating Risks in Proprietary and Open Source AI
Organizations must carefully evaluate the risks associated with both proprietary and open-source AI models during implementation. While established vendors like OpenAI follow existing security practices, uncertainties regarding their decision-making processes and training datasets persist, raising concerns about potential intellectual property violations. In contrast, open-source models present unique vulnerabilities, as they can contain executable code within datasets that may introduce security risks. A thorough review and understanding of the implications of utilizing these models are essential for organizations to safeguard their operations.
Building a Framework for AI Governance and Compliance
Developing a robust framework for AI governance is crucial as organizations increasingly adopt AI technologies amidst evolving regulations. This framework aims to help businesses assess their AI-related risks by outlining security standards and compliance measures tailored to different levels of AI utilization. For instance, critical infrastructure sectors, such as healthcare, require more stringent safety measures compared to general-purpose applications, emphasizing the need for a graded approach. By implementing standards that address these varying degrees of risk, organizations can achieve maturity in their AI practices while fostering innovation and ensuring regulatory compliance.