A context-preserving data layer is the component that sits at the boundary between an organization’s sensitive data and an AI model.
It protects sensitive values before they reach the model while preserving the structure, relationships, and meaning the model needs to reason so the output is still usable in the workflow.
Unlike masking or DLP, which protect the input but destroy the context and leave the output unusable, a context-preserving data layer protects the data and keeps it usable.