Enterprise-class code privacy protection solutions
Refact.ai's local deployment feature solves this conflict perfectly. Specific implementations include:
- environmental preparation: Server configured with 16GB RAM + 4-core CPU, Docker version 18.09+ installed
- Deployment process::
- Execute docker pull smallcloudai/refact
- Set up data volume mapping: docker run -v /path/to/code:/code -p 8008:8008
- Configure access whitelisting and model access rights in the Web UI
- security policy::
- Specify sensitive directories with .refactignore files
- Enable "Code Obfuscation" feature to obfuscate training data
- Configure an enterprise firewall to restrict internal access to port 8008
Financial industry case: After a bank's deployment, AI can only access non-sensitive code in the /src directory, and all training data is kept in local encrypted storage. It is recommended to work with Git's pre-commit hook to automatically scan for sensitive information leakage risks.
This answer comes from the articleRefact.ai: open source AI programming assistant to improve code writing efficiencyThe
May not be reproduced without permission:AI productivity tools " How to solve code privacy issues in an enterprise environment while enjoying the benefits of an AI programming assistant?
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