Enterprises are eager to leverage AI, but the inherent privacy risks of relying on public AI services, particularly for regulated industries, remain a significant barrier. This session will explore the core principles of Private AI – including the strategic use of open-source, private deployments, and controlled data access – and how they form the foundation for building a secure enterprise AI stack in-house. We will delve into the critical components necessary to maintain control over sensitive data and AI workloads, such as the selection of both commercial and open-source AI models that can be hosted within your firewalls, secure infrastructure options (private clouds, on-premise data centers), essential infrastructure like orchestration and high-performance inference engines, model registries for reproducibility and governance, and the vital role of an AI gateway for security, auditability, and access control. Attendees will gain a comprehensive understanding of how these interconnected components, guided by the principles of Private AI, work together to effectively mitigate risks, ensure compliance, and establish a trustworthy and flexible foundation for enterprise AI adoption.

Learning Objectives:
- Understand the core principles of Private AI.
- Ley challenges and risks associated with using public AI services for enterprise data and workloads.
- Components required to build a secure and open private AI stack for production deployment.
- Key considerations when designing a private AI platform.