What is the recommended approach to create a centralized leadership dashboard that combines SCC findings with Cloud Audit Logs using managed services?

• A. Export SCC findings and Cloud Audit Logs to BigQuery; connect Looker Studio to BigQuery and create visualizations/filters.
• B. Cloud Storage + Python + Matplotlib.
• C. Cloud Monitoring custom metrics and log-based metrics with dashboards.
• D. Use SCC dashboard and extract counts from Cloud Audit Logs.

Answer: (A)

Centralizing leadership dashboards works best when you bring all relevant security and governance data into a single managed analytics layer and build the visuals on top of it. Exporting both Security Command Center findings and Cloud Audit Logs into BigQuery creates a single data warehouse where you can freely join, filter, and aggregate across datasets. Then attaching Looker Studio to BigQuery lets you design interactive dashboards with powerful filters, drill-downs, and cross-source analytics—without managing any infrastructure. This combination makes it easy for leadership to see how security findings relate to activity in your cloud environment, compare trends over time, and slice data by project, team, or severity.

Using a different approach tends to introduce more manual work or complexity. Building visuals with local tools like Python and Matplotlib requires you to maintain pipelines and hosting outside of Google’s managed services, which adds maintenance overhead. Relying on Cloud Monitoring dashboards means you’d be focusing on metrics rather than directly correlating the raw findings with audit events, and you’d likely need extra steps to harmonize the data. Relying only on the SCC dashboard and pulling counts from Cloud Audit Logs misses the seamless cross- joining and flexible visual storytelling that a unified BigQuery + Looker Studio setup provides.