What approach supports a centralized leadership dashboard that combines SCC findings with Cloud Logging security events using managed services and supports historical data and joins?

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

Answer: (C)

Focus on tying security findings to the actual event data over time in one place. Exporting SCC findings and Cloud Audit Logs into a single data warehouse lets you keep long-term history and perform joins across datasets to correlate findings with the corresponding log events. BigQuery acts as the managed, scalable store for both sources, so you can retain historical data and run SQL queries that link an SCC finding to the exact timeframe and logs that relate to it.

Then connect a BI tool like Looker Studio to BigQuery to build centralized dashboards with interactive visualizations and filters. This setup provides a single, coherent view for leadership that can drill down into specific findings, filter by time, severity, resource, or project, and perform cross-source analyses without stitching data together manually.

Other approaches fall short because they don’t offer seamless cross-dataset joins or robust historical analysis at scale. Using a local or ad-hoc visualization path (like Python scripts with Cloud Storage) lacks managed, scalable data governance and easy dashboarding. Relying only on standard monitoring metrics or a prebuilt SCC dashboard with counts doesn’t provide the same ability to join SCC findings with Cloud Audit Logs or retain long-term history for trend analysis.