JevMade field notes / Worked recipe

Find database columns that may hold personal information

This recipe combines recognizable data patterns with Jev judgments to identify possible personal information and suggest handling categories.

Original by Jeroen Erne / NexibeoClassification

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Credits and license

“07 · PII column scanner” by Jeroen Erne / Nexibeo. Read the original source.

This expanded guide is an AI-narrated adaptation of the source’s essential explanation, examples and caveats, not a word-for-word reading. The synthetic voice does not imitate the author or imply their endorsement.

License: MIT

MIT License

Copyright (c) 2026 Jeroen Erne (https://nexibeo.com, https://completeaitraining.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Our summary

Organizations can lose track of which database columns contain personal information. This recipe gives Jev a column name and a few sample values, then asks what the column contains, whether it relates to a person, and how sensitive it appears.

Before asking, ordinary software checks recognizable patterns such as email addresses and payment-card checksums. Jev receives those facts along with the sample text. The program uses the resulting sensitivity rating to suggest actions such as masking values, restricting access, or leaving public data unchanged.

A few samples cannot establish what every row contains, and identifying personal data depends on context. Sending real samples to an external service also creates its own privacy decision. This is an inventory aid, not a legal compliance judgment or a substitute for proper data review.

Key takeaways

Use exact pattern checks for facts software can calculate.
Send as little real personal information as possible.
Treat the suggested handling category as something to review.

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