Privacy means controlling how personal, organizational, and sensitive information is collected, accessed, and used. In workplace AI, protect data before sending documents, messages, or code to a service. section 6.2 section 11.1
Use these practical safeguards:
- Choose company-approved tools for sensitive files, and reserve free public services for public information. section 6.2
- Remove secrets, account and server names, personally identifying information, and other unnecessary details from prompts. section 7.5
- Check outputs, preserve versions, record the model and date, and identify who verified the result. section 6.2
- Start coding-tool evaluations with public code or test projects; consider local models when complete data control matters. section 11.1
A simple processing record
Add a header such as “Processed by [AI Model] on [Date], Verified by [Name].” For sensitive records, require additional review and keep sensitive processing in-house when possible. section 6.2
Table 6.2 Ethics in AI document handling
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Ethical concern
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Best practices
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|---|---|
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Data privacy
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Only use AI tools your company approves for sensitive files. Skip free public AI services for work tasks.
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Transparency
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Tell your team when you use AI on shared documents. Be open about how AI helped.
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Human oversight
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Check AI’s work yourself, especially for important documents. AI helps but doesn’t replace your judgment.
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Bias awareness
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Watch for unfair or slanted results. AI can show bias or make mistakes, just like people do.
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Intellectual property
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Be careful when creating content with AI. Make sure you’re not accidentally using someone else’s work.
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Accountability
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Own your work, even when using AI. Don’t blame the AI if something goes wrong.
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Continuous learning
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Keep up with AI best practices. The field changes fast, so stay current on using AI ethically.
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Use the ethics checklist to review data privacy, transparency, human oversight, bias, intellectual property, accountability, and continuous learning. section 6.2
