March 1, 2024 at 04:08PM
The explosive growth in AI will immensely complicate software supply chain security. AI and ML models, integral to AI applications, contribute to the complexity. Developers must understand and secure these models, but existing security tools are ill-equipped for this task. Consequently, a new approach called MLSecOps is needed to address these challenges and stay ahead of potential breaches.
The meeting notes highlight the growing challenge of securing AI software supply chains and the unique security vulnerabilities that AI and machine learning (ML) models introduce. It emphasizes the need for organizations to gain visibility into AI models embedded in their software and to enhance security practices tailored to the AI era. The notes also discuss the need for structured understanding of AI lineage, the development of capabilities to scan and understand AI models, and the introduction of governance, risk, and compliance policies to address insecurities.
The notes also emphasize the emergence of a vendor-neutral movement called MLSecOps, which aims to address AI/ML security challenges in a manner similar to the DevSecOps movement for traditional software. It points out the impact of regulatory mandates on AI/ML security, leading to the creation of new job functions such as the director of AI security.
Overall, the notes underscore the urgency for organizations to adapt their security approaches to the unique characteristics of AI and ML applications, including the development of specialized security capabilities, governance frameworks, and compliance policies to effectively manage the increasing complexities of AI software supply chains.