What counts as sensitive

Sensitivity is contextual. Direct identifiers, linkable details, private communications, credentials, and consequential inferences can all require protection.

Use this three-part method:

1. Classify secrets, personal data, sensitive attributes, and business-confidential material. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
2. Consider combinations that identify someone. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
3. Raise protection when consequences are serious. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.

Walk through A meeting note contains no password, but it includes an employee’s illness, performance concern, salary range, and manager comments.. Label three moments where “What counts as sensitive” changes what you trust: (1) the first fluent answer, (2) the first missing source or permission, and (3) the decision a human must own. Write the before/after task so the model only does the slice that evidence supports. Keep one sentence that states how this page’s idea differs from a generic “AI is smart/dumb” score.

• Looking only for names and email addresses. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Assuming public data is harmless in every context. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Ignoring what a model can infer from several ordinary facts. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.

Before you close the tab, capture a reusable habit for What counts as sensitive inside Privacy and smart sharing: name the observable check, the evidence you would open, and the stop condition. Rehearse it once on a low-stakes example, then once on a higher-stakes variant. The habit succeeds when you can explain the check without reopening this lesson. Target outcome: Recognize personal, sensitive, confidential, and secret information before sharing.

For privacy-smart-sharing, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.

For privacy-smart-sharing, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.

For privacy-smart-sharing, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.