B. BigQuery column-level security: Pros: Granular control over column access, ensures only authorized users see the SSN column. Cons: Doesn't truly redact the data. The SSN values are still stored in BigQuery, even if hidden from unauthorized users. A potential security breach could expose them.

there is no SSN in question, it can be any ID.

Option A does encrypt SSN but requires managing keys separately. Option B relies on complex IAM policy changes instead of encrypting by default. Option C hashes irreversibly, preventing customer service reps from viewing original SSNs when required. Therefore, using DLP format-preserving encryption before BigQuery ingestion balances both security and analytics requirements for SSN data.

No, the value using FPE can be decrypted with key. "Encrypted values can be re-identified using the original cryptographic key and the entire output value, including surrogate annotation." https://cloud.google.com/dlp/docs/pseudonymization#supported-methods

Nevermind, this can actually also be done in the case of answer B. They are both correct, just different implementations. No idea

sorry B

gpt: Option D uses Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token before loading the data into BigQuery. This approach allows for more granular control over data access and can provide an added layer of security. However, it may require additional configuration and implementation effort, and it may also affect the performance of queries on the encrypted data. Google recommends using a combination of data protection techniques to safeguard sensitive data, such as encryption, data masking, and access controls. In this scenario, a possible best practice would be to use both options B and D together to provide multiple layers of protection for the sensitive data while still allowing authorized users to view the original values when necessary.

That is not an argument. Option D does not explain how Customer Services will have access.