Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A straightforward framework for matching each layer of protection to the problem it actually solves. Trying to understand vendors in the modern application security space can feel a lot like trying to solve word scramble. Whether it’s remembering what the “A’s” in "WAAP” stand for, figuring out if “WAF” includes APIs, or assessing the actual function of a new AI security product, understanding what tools your team actually needs is getting harder all the time.

AI is making decisions across your environment — calling APIs, accessing data, and taking action. Most organizations know it's happening, but far fewer can see it, let alone stop it. In this panel discussion, security leaders will share what they've learned deploying and governing AI workloads at scale on AWS, and what it takes to close the gap between deployment and accountability.

Security teams are receiving more alerts tied to AI workloads, but most miss the runtime context needed to understand what happened, why it happened, and whether it violated policy. AI visibility cannot stop at deployment and configuration. Join this live demo session to see how Wallarm AI Hypervisor helps teams understand what AI workloads are actually doing at runtime inside Kubernetes environments. The session focuses on giving security teams clearer operational context around AI behavior, outbound activity, sensitive data exposure, and user-driven actions across AI systems.

AI workloads are appearing across AWS environments faster than most teams can inventory them. New APIs, EKS clusters, model integrations, and AI services are showing up across accounts and regions without a clear ownership trail or centralized visibility. By the time security catches up, the environment has already changed again.

AI adoption within AWS environments is accelerating faster than most security and governance programs. AI agents, APIs, MCP servers, and model integrations are entering production across cloud environments, often without centralized visibility or runtime controls. In this webinar, you’ll see how teams can discover AI workloads across AWS accounts, understand what AI systems are actually doing at runtime, enforce policy in real time, and generate continuous governance evidence without slowing engineering teams down. The session focuses on practical operational capabilities for AI systems already running in production.

In this session, you’ll learn: How to see what your AI systems are actually doing across AWS How to use that visibility to understand behavior and impact How to act in real time with blocking, rate-limiting, and quarantine How to operationalize AI governance with the AI Control Platform.