Protect What Powers Your AI: Risk-First Container Security at Cloud Speed

Kubernetes and containers have become the backbone of modern AI infrastructure, orchestrating GPU-powered LLM workloads across dynamic, distributed environments. But securing these pipelines isn’t just about detection—it’s about staying ahead of risk. With complex layers of infrastructure, ephemeral workloads, and constantly shifting code and API surfaces, AI introduces novel vulnerabilities that traditional approaches can’t keep up with.

That’s why Qualys takes a risk-first approach to container security: identifying, prioritizing, and eliminating risk before it’s exploited.

In this session, we’ll unveil how Qualys is redefining container security for modern AI-driven environments through:

• Unified AI and LLM Discovery: Discover unknown LLMs across containers and Kubernetes running in hybrid cloud environments. Correlate application and infrastructure context to uncover blind spots and enable seamless cyber hygiene.
• Runtime-informed risk reduction: Go beyond alerts with eBPF-powered detection, real-time attack path analysis, and intelligent correlation from code to container to cloud—including signature-free threat classification and zero-trust sandboxing for emerging zero-day activity.
• Proactive security posture management: Scan for prompt injection and API vulnerabilities, and harden your Kubernetes and cloud control planes. Correlate and prioritize vulnerabilities across infrastructure, containers, and APIs using threat context and asset value—so you can focus efforts where they’ll make the biggest impact.
• End-to-end AI workload protection: Enable risk-minded threat detection and response across containers, Lambdas, cloud services, and identities—while tracing runtime risk back to code and ownership to accelerate response across the pipeline.