Emergency management wants to fine-tune on after-action PDFs that still include unredacted personal narratives. What must happen before customization?

Emergency-management after-action PDFs that still include unredacted personal narratives contain sensitive identities and lived experiences from incidents. Before fine-tuning, teams must redact and minimize those narratives so sensitive identities are not absorbed into the customized model and later reproduced in planning assistants or public-facing summaries. Customization without redaction converts privacy-sensitive retrospective documents into a memorization risk for the municipal AI platform.

Increasing temperature during training does not make personal names statistically irrelevant as a privacy control; temperature is a sampling parameter at inference and is not a redaction method for corpus preparation. Converting PDFs to images does not remove privacy risk—vision-language or OCR-capable pipelines can still learn and expose content. Fine-tuning first and relying on residents to request deletion after public leaks inverts the duty: prevention and minimization come before training.

Exam caveat: Privacy for customization data is about corpus content and memorization, not inference hyperparameters or file-format tricks. "Train now, delete later" is not an acceptable municipal pattern when personal narratives from emergencies are involved.

Operational check: Run a redaction workflow approved by emergency management and privacy counsel, verify sample pages for residual names and contact details, store the minimized corpus under restricted access, and evaluate the fine-tuned model with extraction-style prompts aimed at narrative identities. Retain evidence of redaction completion as a release gate before any production customization of emergency-management assistants.