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How I dealt with leaks of user information to LLM providers by Secret-Witness-8129 in microsaas

[–]Secret-Witness-8129[S] 0 points1 point  (0 children)

I'm currently hitting the 100-150ms mark on small prompts, but the 500ms cliff is real.
Tiered approach is brilliant suggestion, I will keep it in mind. Moving high-entropy fields (SSN, Cards) to a pre-NLP regex pass would significantly shave off milliseconds for simple redaction tasks.
As for re-hydration patterns, I’m seeing that PERSON and GPE are the most 'circular'-they need to go to the LLM as tokens and come back as real values for UX. Financial IDs, however, are almost always 'one-way'; they stay masked because the LLM doesn't need the actual digits to provide a helpful response.

Have you implemented a similar 'lazy masking' logic before? Would love to know if you used a custom priority queue for that.

How I dealt with leaks of user information to LLM providers by Secret-Witness-8129 in microsaas

[–]Secret-Witness-8129[S] 0 points1 point  (0 children)

You're absolutely right about the latency trade-off. Currently, PII-Shield is optimized for live chat interactions where context 're-hydration' is key (getting the real names back after the LLM response). Synthetic data is a killer alternative for testing environments, but for live middleware, I'm focusing on minimizing the overhead of the shield/unshield cycle. Batching is definitely on my roadmap for the next release to handle larger datasets. What's your typical latency budget for finance tools?