The correct option is Performing a manual review and redaction pass over tens of millions of source files before uploading them to Cloud Storage. This is not recommended because it does not scale, it introduces human error, and it slows delivery in a high volume environment where automated controls are available and more reliable.

For sensitive datasets at scale, you should use automated data discovery and masking such as Cloud DLP to scan, classify, and redact sensitive fields. You should pair this with strong access controls, encryption, and perimeter protections so that the risk is mitigated through consistent and repeatable policy rather than through labor intensive processes. Automated pipelines also help keep pace with continuous updates to source content which a manual pass cannot match.

Turning on Cloud Audit Logs to capture search requests and data access events for later compliance review is a recommended practice because it provides traceability for administrative and data access operations and supports investigations and compliance reporting. You can route logs to BigQuery or Cloud Storage to meet retention requirements.

Applying VPC Service Controls perimeters around Cloud Storage and BigQuery datasets that feed the search index is recommended because it adds a strong defense against data exfiltration by restricting where protected resources can be accessed from and by limiting interactions with external services.

Enforcing access with Identity and Access Management policies that restrict which staff groups can query the application aligns with the principle of least privilege. You can use IAM roles and group based assignments, and you can add IAM Conditions to narrow access based on attributes so that only authorized users can query sensitive content.

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