{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6a640e498c6152b11fc62b42?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Strip Sensitive Files So AI Never Sees the Private Parts","description":"<p>What do you do when AI could help with a document—but the document is too sensitive to upload?</p><p><br></p><p>Airlock, a local workflow for separating the information a task genuinely needs from the private or confidential material a file happens to contain. I walk through protected terms, default-hide review, rebuilding a clean copy instead of merely drawing redaction bars, and the judgment call at the center of safe AI work: start with the job, not the file.</p><p><br></p><p>The episode also explores why this problem has become urgent as AI workflows absorb more real proposals, contracts, meeting notes, and code; what Verizon’s 2026 DBIR says about AI use on corporate devices; and why NIST’s idea of “security fatigue” helps explain the appeal of the fastest upload path.</p><p>Key takeaway: useful AI context and sensitive information are often bundled together, but they are not the same thing.</p><p><br></p>","author_name":"Nate B. Jones"}