The Blind Spots in AI Security That Could Cost Us All

Brian Stevens, SVP & CTO – AI, Red Hat, Explains the Unsolved Challenges

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Enterprises today aren't struggling to build AI models. They're struggling to operationalize and scale them securely across hybrid environments. The “last mile of AI adoption” has emerged as one of the biggest barriers to enterprise success, slowing down time-to-value and creating inefficiencies in deployment.

Brian Stevens, SVP & CTO–AI at Red Hat, is tackling this challenge head-on with the Red Hat AI Inference Server, built on the open-source vLLM project and enhanced with Neural Magic optimizations.

In this conversation, he explains why open source is the foundation of enterprise trust in AI, how organizations can bridge the gap between experimentation and scale, and the evolving skill sets leaders and developers need to thrive in an AI-powered enterprise future.

With Red Hat's AI Inference Server, how are you reimagining the last mile of AI adoption, taking models from lab experiments to enterprise-scale, secure deployments?

The “last mile” of AI adoption is where most enterprise initiatives stall. Organizations can build successful proofs-of-concept around specific use cases, but scaling these models to serve thousands of users and agents while maintaining security, performance and operational efficiency is the real challenge.

Red Hat's AI Inference Server reimagines this by treating AI deployment as a journey across hybrid environments. Built on the proven vLLM project and enhanced with Neural Magic's optimization technologies, our approach addresses three critical gaps: infrastructure reality, economic sustainability, and security without compromise.