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Whitepaper

Securing AI Where It Executes

Most AI security guidance concentrates on what happens before a model reaches production: reviewing training data, evaluating model behavior, and setting policy for acceptable use. That work matters, but it cannot account for what an AI workload actually does once it is running in a live environment with access to real data and real systems. This whitepaper makes the case for securing AI where it executes, examining the runtime signals that reveal a compromised or manipulated AI workload and the visibility gaps that open up when models run across cloud services, containers, and endpoints that each carry their own security posture. It covers the telemetry security teams need from AI runtime environments, how that telemetry folds into existing detection and response workflows, and what an effective containment action looks like when the compromised asset is a model rather than a host.