Why I built a privacy-first LLM proxy
📰 Dev.to · ChrisRemo
Learn how to build a privacy-first LLM proxy to protect your prompts from being logged, and why it matters for secure AI interactions
Action Steps
- Evaluate existing LLM gateways for their logging policies
- Spin up a proxy server to intercept and modify LLM requests
- Configure the proxy to remove or anonymize prompt data
- Test the proxy with various LLM gateways to ensure compatibility
- Deploy the proxy in a production environment to protect sensitive prompts
Who Needs to Know This
Developers and data scientists working with LLMs can benefit from this approach to ensure the privacy and security of their prompts and data
Key Insight
💡 Existing LLM gateways often log user prompts, compromising privacy and security
Share This
🔒 Protect your LLM prompts from logging with a privacy-first proxy! 🤖
Key Takeaways
Learn how to build a privacy-first LLM proxy to protect your prompts from being logged, and why it matters for secure AI interactions
Full Article
Every LLM gateway I evaluated had the same problem: they logged my prompts. I'd spin up a proxy,...
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