PII Masking, Audits, and Post-Market Monitoring
AI systems require care after launch, not just before launch.
Plan for PII handling, auditability, and continuous monitoring after deployment.
The lesson is public. The pressure loop lives inside the app where submissions, revision, and AI review happen.
An evaluation scorecard and post-launch monitoring plan.
Each lesson contributes to a week-level artifact and eventually to the shipped AI-native SaaS.
PII Masking, Audits, and Post-Market Monitoring
This lesson brings governance into the operating loop: handling sensitive data, preserving auditability, and defining what to watch after launch.
Many teams think launch is the finish line. For AI products, launch is the beginning of continuous evidence collection about drift, misuse, and user harm.
Post-market monitoring means you assume the system will surprise you. Your job is to create the telemetry, review loop, and intervention paths needed when it does.
What the machine covers in this lesson.
This lesson brings governance into the operating loop: handling sensitive data, preserving auditability, and defining what to watch after launch.
Many teams think launch is the finish line. For AI products, launch is the beginning of continuous evidence collection about drift, misuse, and user harm.
Post-market monitoring means you assume the system will surprise you. Your job is to create the telemetry, review loop, and intervention paths needed when it does.
PII handling matters because traces, prompts, and review content may capture sensitive information. Auditability matters because you need to explain which model version, prompt version, rubric, and evidence path led to a result. Monitoring matters because quality can drift with prompt changes, context changes, or user behavior changes. Operational maturity means you plan reviews, thresholds, and rollback triggers before they are needed.
A lesson-review system starts receiving pasted customer data inside submissions. A good design masks or redacts before sending to the model, preserves enough metadata for internal review, and alerts the team when patterns suggest policy misuse.
Common failures include retaining everything indefinitely, logging raw sensitive text by default, and having no owner or cadence for post-launch review.
Further reading the machine expects you to use properly.
OWASP User Privacy Protection
Useful for redaction and privacy-aware logging.
Open referenceEU AI Act Overview
Use this as policy context, even if the product is not high-risk today.
Open referenceNIST AI RMF Govern Function
Tie monitoring and governance to a formal framework.
Open referenceThe full lesson is inside the app.
Submit the exercise, receive AI review, close the gaps the machine finds, and unlock the next lesson in the sequence.