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Google Professional Cloud Security Engineer: How to Securely Design AI/ML Pipelines with Sensitive Data in Google Cloud?

By: Author Alex Lim

Posted on

Categories Exam

Learn best practices for securing sensitive data in AI/ML pipelines on Google Cloud Platform. Leverage Cloud DLP for data de-identification and IAM for access control to BigQuery datasets.

Table of Contents

Question

Your organization is developing a sophisticated machine learning (ML) model to predict customer behavior for targeted marketing campaigns. The BigQuery dataset used for training includes sensitive personal information. You must design the security controls around the AI/ML pipeline. Data privacy must be maintained throughout the model’s lifecycle and you must ensure that personal data is not used in the training process. Additionally, you must restrict access to the dataset to an authorized subset of people only. What should you do?

A. De-identify sensitive data before model training by using Cloud Data Loss Prevention (DLP)APIs. and implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
B. Implement Identity-Aware Proxy to enforce context-aware access to BigQuery and models based on user identity and device.
C. Implement at-rest encryption by using customer-managed encryption keys (CMEK) for the pipeline. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
D. Deploy the model on Confidential VMs for enhanced protection of data and code while in use. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.

Answer

A. De-identify sensitive data before model training by using Cloud Data Loss Prevention (DLP)APIs. and implement strict Identity and Access Management (IAM) policies to control access to BigQuery.

Explanation

To securely design the AI/ML pipeline while maintaining data privacy and restricting access to sensitive data in BigQuery, you should:

  1. Use Cloud Data Loss Prevention (DLP) APIs to de-identify sensitive personal information before using the data for model training. Cloud DLP can detect and redact sensitive data elements like names, addresses, credit card numbers, etc. This ensures that no personal data is directly used in the model training process.
  2. Implement strict Identity and Access Management (IAM) policies to control access to the BigQuery dataset containing sensitive information. Define fine-grained permissions to allow only authorized users to query or export the data. Assign roles like “BigQuery Data Viewer” or “BigQuery Job User” to the specific individuals who need access.
  3. For additional security, consider using Cloud Key Management Service (KMS) to encrypt the BigQuery data at-rest with customer-managed encryption keys (CMEK). This provides an extra layer of protection for sensitive data.
  4. Regularly audit IAM policies and BigQuery jobs to ensure compliance and detect any unauthorized access attempts. Use Cloud Logging and Monitoring to track and alert on suspicious activities.

By de-identifying sensitive data with Cloud DLP and enforcing granular access controls through IAM, you can maintain data privacy while allowing the ML model to learn from the dataset. The combination of these security measures helps protect sensitive information throughout the AI/ML pipeline lifecycle.

Google Professional Cloud Security Engineer certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Google Professional Cloud Security Engineer exam and earn Google Professional Cloud Security Engineer certification.