From a business perspective, this makes sense. AI systems perform best when they are grounded in real organizational knowledge. From a security perspective, however, it represents a fundamental change in how sensitive data is handled. Information that was once confined to controlled repositories is now being copied, transformed and transmitted as part of inference requests.
Unlike traditional data flows, prompts are rarely classified, sanitized or monitored. They pass through application layers, middleware, logging systems, observability pipelines and third-party services with minimal scrutiny. In many cases, they are treated as operational exhaust rather than as high-value data.
This creates a dangerous mismatch: some of the most sensitive data in the organization is flowing through one of the least protected pipelines.
