Cloud Dlp Classify Phi Pii Custom Infotypes Rulesets

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An organization is streaming application logs from a clinical healthcare microservice into Google Cloud. During a security audit, you identify that unstructured log messages contain both direct identifiers—specifically proprietary Medical Record Numbers (MRNs) following the pattern ###-#-#####—and potential quasi-identifiers such as person names that could expose Protected Health Information (PHI).

You need to design a Sensitive Data Protection (Cloud DLP) inspection strategy that accurately identifies and classifies these distinct data categories while minimizing false positives from standard application terms.

Which inspection configuration should you implement?

Deploy a BigQuery ML logistic regression classification model directly onto the log router stream to categorize log lines into binary categories of sensitive and non-sensitive.

Create a custom regular expression (regex) infoType detector for the MRN pattern, apply the built-in PERSON_NAME detector augmented with hotword rules that increase likelihood when clinical context words appear, and add exclusion rules to filter out standard system tokens.

Implement an exclusion rule that suppresses all PERSON_NAME findings unless matched by a regular custom dictionary, and assign a fixed likelihood of VERY_LIKELY to every numeric sequence matching ###-#-#####.

Configure a stored custom dictionary detector containing static regular expressions for MRNs, and configure default global inspection rules to lower the likelihood of all PERSON_NAME findings across all service log streams.

Professional Cloud DevOps Engineer

Cloud Dlp Classify Phi Pii Custom Infotypes Rulesets

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