Securing AI workloads at runtime: GPU clusters, containers, and agents in production

About this event

Your infrastructure looks nothing like it did 18 months ago. Training and inference run across GPU clusters that scale by the hour, often across more than one cloud and more than one provider. Engineers pull open-source models, vector databases, and orchestration frameworks into production faster than anyone can review them. Agents hold credentials, call internal APIs, and execute code. Model weights, training data, and customer data often sit in the same account under different labels. Most security tooling was bought to answer a different question. It reports how a resource was configured at the last scan. It does not tell you what is executing in a container that lives for four minutes, which identity called which service, or whether the process on your inference node is the one you shipped. At the same time, enterprise customers send questionnaires written for companies that deploy quarterly, and your security team is outnumbered by engineering 20 or 30 to 1.

• What is actually running: getting runtime visibility into GPU clusters, short-lived containers and inference nodes.
• Identity for agents: credentials, internal API calls and code execution without a human in the loop.
• Open-source models, vector databases and orchestration frameworks entering production faster than review cycles.
• Model weights, training data and customer data in the same account under different labels.