{"id": "TTC-104", "slug": "everyday-privacy-and-data-minimization", "title": "Everyday privacy and data minimization", "level": "beginner", "summary": "Give an AI workflow only the data needed for the stated purpose, remove identifiers when possible, and define retention and access before sharing anything.", "learning_outcome": "Minimize a fictional dataset for one task and justify every retained field against the purpose.", "explanation": "Start with purpose: what exact output is needed, and which fields are essential to produce it? Delete, mask, aggregate, or replace everything else with synthetic values. A name removed from a record may still be recoverable from dates, locations, or rare combinations, so consider indirect identification. Check the provider, storage location, retention, access, and deletion path before disclosure. Prefer a local summary or selected excerpt over a complete file. Record what was released and withheld. When the purpose changes, reassess it; prior access is not blanket permission for a new use.", "worked_example": "Fictional case: a bakery wants themes from staff survey comments. Jo removes names, email addresses, exact shift times, and references to medical leave; substitutes department codes only where comparison is needed; and supplies the comments in a temporary file. The receipt lists four released and four withheld fields.", "exercise": "Take a synthetic ten-field record and a precise summarization purpose. Create a release table with keep, transform, or withhold for every field; produce the minimized input; then set an access and deletion rule. Repeat with a changed purpose and show why the release decision changes.", "success_criteria": ["Every released field has a purpose-linked justification.", "Direct identifiers and unnecessary sensitive or linkable details are absent from the minimized input.", "The exercise records access, retention, deletion, and the fields deliberately withheld."], "limitations": ["Minimization reduces exposure but cannot guarantee anonymity or eliminate provider and recipient risk.", "Legal obligations depend on jurisdiction and context; this lesson is not legal advice."], "prerequisites": ["TTC-101"], "next_lessons": ["TTC-105", "TTC-116"], "copyable_material": "# TTC-104 \u2014 Everyday privacy and data minimization\nObjective: Release the smallest useful input for one declared purpose.\nProcedure: Classify each field as keep, transform, or withhold; document access, retention, and deletion before use.\nRequired evidence: Produce a receipt listing purpose, released fields, withheld fields, transformations, and expiry.\nBoundaries: A previous release does not authorize reuse; stop when identity or sensitivity cannot be reduced enough.\nCompletion test: The task remains possible with the minimized input and no retained field lacks a purpose.\nReview rule: Treat generated work as a draft until the named human reviewer accepts it.", "sources": [{"title": "Regulation (EU) 2016/679 (General Data Protection Regulation)", "publisher": "European Union", "url": "https://eur-lex.europa.eu/eli/reg/2016/679/oj"}, {"title": "NIST Privacy Framework", "publisher": "National Institute of Standards and Technology", "url": "https://www.nist.gov/privacy-framework"}], "version": "1.0.0", "reviewed_on": "2026-10-03", "review_status": "reviewed", "next_review_criteria": "Privacy law, provider data handling, or the product's retention and deletion behavior changes.; A cited primary source is materially revised, replaced, or becomes unavailable.; Repeated learner results show that the exercise or success criteria are ambiguous.", "canonical_aliases": ["/ai-agent-security/"], "canonical_url": "https://teachthecompany.com/school/everyday-privacy-and-data-minimization/"}