For homegrown models, Falcon Cloud Security surfaces training data, model artifacts, experiment scripts, and package dependencies. This level of detail helps teams track model provenance, enforce governance, and reduce shadow AI.
For off-the-shelf capabilities like Bedrock or Vertex AI, Falcon Cloud Security identifies the APIs and services being accessed. It brings these embedded models, which are often abstracted away from developers, into focus so teams can uncover what risks may be running behind the scenes.
Still, identifying risks in code and cloud platforms isn’t enough. For lasting protection, organizations must know what’s running at any given moment.
Maintaining a Real-Time Inventory of AI Workloads
Even with proper scans and policies in place, AI workloads in production can drift. Containers scale dynamically, teams experiment with new models, and services evolve quickly. That is why runtime visibility is critical.
Falcon Cloud Security delivers a real-time inventory of AI-related assets running across AWS, Azure, and Google Cloud environments. This includes Kubernetes clusters, containers invoking AI services, and models running via cloud-native APIs.
This runtime inventory ties back to earlier pipeline detections. Teams can easily trace risk from build time to runtime — if a container flagged for AI vulnerabilities during development is now actively running in production, Falcon Cloud Security connects the dots. This enables teams to take swift, informed action.
