Working safely with Copilot: Why data labeling is crucial

🚧 The risks of mislabeled data

• AI sees no difference without labels 🏷️
  Copilot processes data without distinguishing between ordinary and sensitive data unless it is explicitly marked as such. This means that personal data or confidential documents can just pop up in analyses.

• Outdated information increases risks ⏳
  Many organizations, especially municipalities, keep outdated data. The Data Protection Impact Assessment (DPIA) warns that this can lead to inaccurate data being processed, with dire consequences.

• Privacy problems with inspection requests 🔎
  Citizens have a right to know what data is being processed about them. But when Copilot is used to handle these requests, it can produce incomplete or incomprehensible results. This undermines the transparency principle.

• Loss of control over data ⚠️
  When organizations rely too much on AI without human control, sensitive data can be unintentionally shared or misinterpreted. This can even have socioeconomic consequences, for example, if misinformation leads to wrong decisions about benefits or allowances.

 

 

✅ How do you avoid these risks?

🔹 Classify and label data: Make sure sensitive data is properly labeled so AI systems can take it into account.¹
🔹 Regularly clean up outdated data: Remove unnecessary and erroneous data to improve data quality.²
🔹 Human control and validation: Do not let AI make unsupervised decisions about sensitive information.³
🔹 Implement DLP policies: Use Data Loss Prevention (DLP) solutions to prevent unwanted sharing or leakage of sensitive information.⁴
🔹 Training and awareness: Make sure employees understand how AI works and the risks involved in handling privacy-sensitive data.
🔹 Review privacy policies: Adapt internal guidelines to the use of AI and provide a DPIA when implementing tools such as Copilot.

 

🎯 Conclusion

Copilot offers many benefits, but without proper data management strategies, it can also pose risks. Municipalities⁵ and other organizations must have their information housekeeping in order and label sensitive data correctly to avoid privacy issues and wrong AI decisions. After all, AI is smart, but not without human guidance. 🤖💡

 

👉 How does your organization handle AI and sensitive data?

 

References

¹https://alta-ict.nl/blog/beveilig-je-data-met-microsoft-purview-classificaties-en-vertrouwelijkheidslabels/

²https://alta-ict.nl/blog/lifecycle-management-in-microsoft-365-en-azure/

³https://alta-ict.nl/blog/hoe-werkt-microsoft-purview-insider-risk-management/

⁴https://alta-ict.nl/blog/bescherm-je-gevoelige-data-met-dlp-in-microsoft-365/

⁵https://alta-ict.nl/blog/moet-je-je-ai-copilot-gebruik-beperken-microsoft-zegt-van-wel/

 

 

About the author

My name is Alta Martes, a specialist in Microsoft 365 and Google Workspace, with a focus on modern workplace management, cloud security and identity & access management. With years of experience, I help organizations optimize their IT infrastructure and create a secure, efficient digital workplace.

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