Claude Academy
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Understanding privacy and data

Lesson 530 min
In this lessonBy the end, you’ll be able to
  • Articulate privacy concerns and evaluate AI tools based on their data handling policies
  • Practice data hygiene strategies for safely working with sensitive information

Understanding privacy and data

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Understanding privacy and data · 10 min

SummaryTranscript

This video addresses one of the most common concerns nonprofit professionals have about AI: data privacy. You'll learn what actually happens to data you share with AI tools, how to evaluate different platforms and plans for their privacy protections, and how to prepare sensitive data for safe AI use.

Key takeaways

  • AI introduces new privacy considerations—particularly around training: Some AI uses your inputs to train future models, which means patterns from your data could influence future outputs.
  • Different tools have different rules: The free AI you use to brainstorm event themes is not the same as a paid account with strict data retention policies. Match your tool to your task—tools with more protection allow for safer sharing of sensitive data.
  • Safe AI use isn't about avoiding it—it's about using it responsibly: Apply Problem Awareness and Platform Awareness before starting a new project. Often you can get full benefit from AI without sharing sensitive information by breaking tasks into component parts
  • You can often remove identifying information entirely: For pattern analysis, you likely don't need names, contact details, or other PII. Work backwards from your actual goal to determine what data is truly necessary
  • If something goes wrong, you have options: Delete the conversation, request data deletion through the platform's privacy process, and follow your organization's protocols

Exercises

Evaluating data sensitivity

This exercise helps you develop judgment about what data is safe to share with AI tools.

Part I: Review sample data

Choose one of the following scenarios that's most relevant to your work:

  • A spreadsheet of donor giving history with names, amounts, and contact information
  • Survey responses from program participants including demographic details
  • A grant report draft containing beneficiary stories and outcome data

Part II: Annotate for sensitivity

For your chosen scenario, identify:

  • Which fields or sections contain personally identifiable information (PII)?
  • Which information is essential for the analysis you want to do?
  • Which information could be removed or anonymized without losing analytical value?
  • What's the worst-case scenario if this data were exposed?

Part III: Plan your approach

  • What would you remove or modify before sharing with AI?
  • What tool/plan tier would be appropriate for this level of sensitivity?
  • What verification steps would you take after receiving AI's analysis?

Lesson reflection

  • How does thinking about AI privacy compare to how you already think about other software tools (email, cloud storage, CRMs)?
  • What's one change you'll make to how you approach sharing data with AI based on this lesson?

What's next

In the next lesson, we'll put these privacy practices into action as we explore data analysis with AI—learning how to spot patterns, generate insights, and strengthen your programs while keeping sensitive information protected.