Training LLMs on unstructured data means either leaving out 80% of your most useful information, or risking exposure of PII, PHI, and IP in the process. This whitepaper breaks down where sensitive data actually lives in the LLM lifecycle, and how selective encryption lets you use the data without the exposure.
Where sensitive data hides across the eight steps of the LLM lifecycle, from ingestion to deployment
Why selective, field-level encryption beats redaction, anonymization, and vendor-dependent sanitization
How TEEs, MPC, and FHE fit in, and where each one still leaves sensitive data exposed
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Where sensitive data actually lives across the eight steps of training and deployment, from data source integration through post-deployment monitoring.
Why encrypting specific words and paragraphs, not entire files, lets you train on 100% of your data without exposing what shouldn't leave the building.
Internal sanitization, vendor tools, and privacy-enhancing tech like TEEs, MPC, and FHE all have real limits, and none of them fully solve the problem alone.