Evaluating Enterprise AI Security: Questions Every Buyer Should Be Able to Answer

In our previous white paper entitled AI Security Beyond the Model, NSS Labs outlined why securing the AI model alone is insufficient, and why enterprise AI security must be treated as a system-level and governance challenge. The real security challenges emerge in the systems surrounding the model – where data is retrieved, tools are invoked, and automated decisions are executed.

This paper builds on that foundation by moving from understanding the problem to evaluating potential solutions and helping enterprise buyers formulate better questions when shortlisting AI security vendors. Consistent with AI Security Beyond the Model, this paper focuses primarily on runtime guardrails (the controls outside the model that enforce policy, protect data, and produce audit evidence) while recognizing that model security/Responsible AI reduces intrinsic model risk but cannot manage enterprise interaction risk alone. As AI systems become embedded in enterprise workflows, security decisions increasingly intersect with governance, risk management, and regulatory accountability. Organizations must be able to explain not only how AI systems function, but how risks are controlled, monitored, and audited.