News, updates, and insights from the NOI team.
Compare tokenization, masking, and redaction for AI prompt protection. See why deterministic tokenization preserves LLM reasoning while masking and redaction break it.
By Naomi Stevens
OpenAI's Privacy Filter is a strong open-weight PII detection model, but it redacts irreversibly, covers only eight categories, and is not a proxy. Production LLM applications need reversible tokenization, persistent entity mapping across turns, multi-provider proxying, and regulated identifier coverage, none of which Privacy Filter provides. NOI operates in that layer of the stack, and the OpenAI release makes the case for that layer sharper, not weaker.
By Naomi Stevens
Compare tokenization, masking, and redaction for AI prompt protection. See why deterministic tokenization preserves LLM reasoning while masking and redaction break it.
PII tokenization for AI is the process of replacing personally identifiable information in LLM prompts with deterministic, reversible tokens before the data reaches a model provider. The original values are stored securely in a vault. The model receives a sanitized version of the prompt that preserves structure and relationships, allowing it to reason effectively without ever seeing real sensitive data. When the response returns, the tokens are replaced with the original values before the application receives them.
By Naomi Stevens
Enterprises want to use AI but can't send customer PII to third-party LLMs without violating compliance requirements. NOI is a drop-in reverse proxy that tokenizes sensitive data before it reaches any LLM provider - and it takes one line of code to set up.