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AI workloads are dynamic, abstracted, and deeply embedded into modern applications, making them hard to track with traditional security tools. This creates a growing “shadow AI” problem where models, agents, and tools operate without oversight. Our AI workload discovery solves this by continuously analyzing cloud-native logs and runtime signals to identify all AI components in your environment—including hosted services, models, tools, and MCP servers. Each component is correlated to the workload executing it, the identity behind it, and its behavior over time. By baselining this activity, security teams can quickly spot anomalies such as new models, unauthorized integrations, or unusual usage patterns. More importantly, this visibility feeds directly into detection and response, enabling full attack storylines that include the AI layer.