Title: Discussion 8 — Setting Your PII Recall Bar
Course: AIINFRA 302: AI Security, Guardrails & Governance > Section: Week 08 — PII Protection with Presidio and Output Validation

💬 Discussion 8 — Setting Your PII Recall Bar (20 pts)

Prompt: Presidio's recall depends heavily on the NLP backend and tuning you choose — it will never catch 100% of PII. Imagine you're responsible for PII redaction in two very different systems: (1) a healthcare chatbot that summarizes patient messages for a care team, and (2) a marketing chatbot that answers questions about a retail brand's products. In a post of at least 200 words, address:

- What false-negative rate (missed PII) would you consider acceptable for each system, and why is that acceptable rate so different between the two?
- What specific tuning or design choices (recognizer selection, confidence thresholds, custom recognizers, human review, multi-point scanning) would you use to hit your target in the healthcare case?
- Where would you personally draw the line between "automate the redaction" and "require a human to review before release"?

Then respond to at least two peers. When you respond, push on a specific number or design choice they made — do you think their acceptable false-negative rate is too permissive or too conservative for the scenario they described, and why?