Redact Sensitive Files Before AI Sees Them: The 'Start With the Job' Method

Key Takeaways

• Sensitive work is still work. Warning people off AI just hands the problem back to them. The goal is to redact without destroying the context that makes the model useful.
• Start with the job, not the file. Define what the model needs to answer your specific question (e.g. "what assumptions could break this plan?"), then remove everything that only came "along for the ride" — customer names, home addresses, private medical notes, API keys, unreleased prices.
• Define protected terms up front. An internal codename like "Project Lantern" doesn't look private to a machine but is highly confidential. Context often lives only in your head, so you have to supply it to any redaction tool.
• Default to hide; keep on purpose. Anything uncertain is hidden by default — you have to deliberately decide to keep it, not the other way around.
• Rebuild, don't black-box. Word files quietly retain comments, track changes, author names, old edits, and external relationships. A safe approach outputs a fresh document from the approved material and leaves the original untouched on your machine — not black rectangles over the old file.
• The same fact can be essential or irrelevant depending on the question. A negotiated price matters if you're evaluating pricing; it's noise if you only want the operational assumptions. Intent decides.
• Why it's newly urgent: AI 100×'d the data we want to move, and sensitive info is intermixed with the useful context. Verizon telemetry cited in the video: corporate-device AI use rose 15%→45%, two-thirds via non-company accounts, with source code the most-submitted material. "Security fatigue" means the easy path wins — so privacy has to live in the same path as convenience.
• Know when redaction is the wrong tool. If removing the sensitive data removes the task itself (e.g. a full medical history that is the analysis), the work belongs in a governed environment — or not with AI at all.