Dear community,
I would like to present a method for discussion that allows us to use AI tools in our development work while maintaining data privacy.
BASIC PROCEDURE:
- Anonymization: Before sending to AI tools, we replace all sensitive information with placeholders.
- AI processing: The AI works with the anonymized data.
- Recovery: After receiving the AI response, we replace the placeholders with the original data.
Note: New placeholders are created for each planned AI processing.
PLACEHOLDER METHODS:
- Semi-generic placeholders:
- Example: Person_1, Company_A, Product_X
- Evaluation: Easy to implement, clearly recognizable as placeholders, but not very realistic
- Random names from predefined lists:
- Example: Max MĂĽller, Bergstadt GmbH, SyncPro
- Evaluation: More realistic, retains cultural context, requires maintenance of the lists
- AI-generated names:
- Example: Lena Bergmann, NexTech Solutions, DataFlow Pro
- Evaluation: High creativity and context customization, maybe technically demanding
IMPLEMENTATION IN FILEMAKER:
All necessary routines for placeholder generation and recovery are implemented directly in FileMaker. This includes:
- Scripts for recognizing and replacing sensitive data
- Management of name lists for random selection
- Integration of an AI API for name generation (optional)
- Mapping tables for assigning placeholders and original data
ADVANTAGES OF THIS APPROACH:
- Maintaining the confidentiality of customer or company data
- Flexibility in the choice of placeholder method
- Full integration into the FileMaker workflow
QUESTIONS FOR THE COMMUNITY:
- What experiences have you had with similar approaches?
- How do you rate the different placeholder methods?
- What challenges do you see when implementing them in FileMaker?
- Do you have any ideas for optimizing or expanding this concept?
- Do you have any concerns about this approach in terms of data protection and confidentiality?
I look forward to hearing your thoughts and experiences on this topic!
~Udo ( @mipiano )