Once sensitive data reaches a model provider, no later decision can bring it back. The layer that detects and masks that data before any model call deserves more design time than model selection. In the AI systems I build, designing the masking layer was among the hardest parts of the work, harder than choosing the models behind it.

Key takeaways

A masking layer detects and replaces sensitive data before any text reaches a language model, local or external.

Masking belongs at the boundary of the pipeline, as one service in front of every model call, not code repeated inside each feature.

Detection does not need a large model.…