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Answer: Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention (Cloud DLP) API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
The most efficient and scalable solution is to build a Cloud Function that utilizes the Cloud Data Loss Prevention (Cloud DLP) API. This approach allows for real-time analysis of data, leveraging tagging and confidence levels to either pass or quarantine data for review. It is cloud-native and integrates seamlessly with other Google Cloud services. Option A is less effective as it only restricts access post-transmission. Option B is reactive rather than preventive, analyzing data after it has been processed. Option C involves third-party tools, which may not offer the same level of integration and scalability as native Google Cloud services.
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You are tasked with developing a scalable, cloud-native solution to prevent the transmission of personally identifiable information (PII) from handheld scanners to analytics systems for a shipping company. Which approach should you take?
A
Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B
Use Cloud Logging to analyze the data passed through the entire pipeline to identify transactions that may contain sensitive information.
C
Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
D
Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention (Cloud DLP) API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.