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Model governance is crucial as enterprises adopt AI, requiring secure and consistent model behavior. This presentation by Tasha Drew of Broadcom VMware focuses on the challenges of achieving model governance and how VMware Private AI’s model gallery addresses these challenges through its capabilities and workflows. The core issue highlighted is the risk associated with introducing models into enterprise environments, similar to the security concerns surrounding containers in their early adoption. This necessitates robust security protocols and consistent monitoring to prevent vulnerabilities and ensure the models operate as intended.
A key aspect of the presentation emphasizes the growing importance of “agentic workflows,” where Large Language Models (LLMs) act as interfaces, orchestrating interactions with various tools and agents to achieve more accurate and comprehensive results. The example of a sales agent leveraging multiple data sources (public internet, internal documents, CRM systems) to generate a compelling presentation illustrates this concept. This highlights the complexity of integrating AI into business processes and the need for robust governance to manage the multiple data sources and agents involved.
The presentation then details how VMware Private AI Foundation, integrated with NVIDIA, helps achieve model governance. This includes a demo showcasing a workflow from model import (from sources like Hugging Face) through security testing (using tools like Giscard) to deployment in a secure environment (Harbor). This integrated approach allows for programmatic model evaluation, monitoring for behavioral drift, and controlled access through versioning and access control mechanisms. The ultimate goal is to enable enterprises to safely adopt AI by operationalizing security testing and providing a centralized, auditable repository for their AI models, thereby minimizing risks and maximizing the benefits of AI within their organizations.
Personnel: Tasha Drew
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