Redacting Personal Data: A Speed and Accuracy Comparison of Regex, Presidio and Hybrid Designs

arXiv cs.AINovember 6, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2611.07633v1 Announce Type: new Abstract: Many enterprise projects now pass free text to large language models (LLMs), and that text often contains personally identifiable information (PII) such as names, phone numbers, email addresses, home addresses and dates of birth. Once a prompt is sent, this data is copied into provider systems, application logs and observability traces. Redacting PII before the call is an obvious control, yet teams seldom have numbers on what it costs in latency or what it lets through. This paper compares four redaction designs that any LLM pipeline can adopt: a pure regular-expression (regex) redactor, Microsoft Presidio running on a spaCy model, a hybrid in

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