Large language models in public-facing industrial applications must accurately process data for the domain in which they are deployed, but they must not leak sensitive or confidential information when used. We present a process for anonymizing training data, a framework for quantitatively and qualitatively assessing the effectiveness of this process, and an assessment of the effectiveness of models fine-tuned on anonymized data in comparison with commercially available LLM APIs.