QuestionQ60

Observability

You are a security engineer at a financial technology company. You need to build a centralized dashboard that gives your leadership team visibility into security posture. The dashboard must meet these requirements:

  • Provide insights from Security Command Center (SCC) findings and security-related events recorded in Cloud Logging.
  • Support high volumes of historical data.
  • Be capable of joining SCC findings with audit logs.

You want to use the most effective visualization solution that relies on Google Cloud managed services. What should you do?

  • A Ingest the SCC findings and Cloud Audit Logs into a Cloud Storage bucket. Write a Python script that reads the data and uses Matplotlib to create the visualizations.
  • B Create custom metrics in Cloud Monitoring based on the SCC findings, and configure log-based metrics for security-related events. Build Cloud Monitoring dashboards to visualize these custom and log-based metrics.
  • C Export SCC findings and Cloud Audit Logs to BigQuery. Connect Looker Studio to the BigQuery datasets, and create the visualizations and filters.
  • D Use the built-in SCC dashboard to visualize the SCC finding, and extract log counts for specific log events from Cloud Audit Logs.
Explanation

BigQuery is a managed, petabyte-scale analytics warehouse that supports analysis and custom reporting on large historical datasets. Exporting Security Command Center findings and Cloud Audit Logs into BigQuery makes both datasets queryable with SQL, including joins between findings and audit-log data. Looker Studio can connect to those BigQuery datasets to provide interactive visualizations and filters for a centralized dashboard.

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