Security leaders want generative AI that advances innovation without expanding risk.

Organizations face three critical challenges when deploying gen AI: security posture gaps from proliferating AI tools accessing sensitive data, quality gaps requiring higher accuracy than out-of-the-box performance, and efficiency constraints from resource-intensive large models. Traditional approaches rely on commercial APIs that create data exfiltration risks and vendor lock-in, whereas production-ready architectures require observability for tracking AI calls and data flows.

In this session, led by Devvret Rishi of Rubrik, you will learn:

• How to implement observability, prevention and remediation pillars for secure AI deployment;
• Which fine-tuning strategies achieve superior performance with smaller, cost-effective models;
• How to build governance frameworks that enable innovation while maintaining security controls.