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A fintech startup processes transaction logs and customer account records that are stored in an Amazon S3 bucket. The bucket is encrypted using server-side encryption with AWS KMS keys (SSE-KMS). Some of the stored data contains sensitive financial attributes such as customer bank account numbers, credit scores, and national ID numbers. To ensure data privacy and regulatory compliance, the company must automatically detect, mask, and control access to sensitive fields during the analytics workflow. Analysts need masked versions of the data for modeling, while certain pre-processing engineers require controlled access to unmasked data for enrichment tasks. The company wants a low-maintenance and scalable solution that ensures end-to-end protection of sensitive data within the pipeline. Which combination of solutions should the company use to fulfill these requirements? (Select two)