A financial services firm is deploying an AI model for fraud detection that requires rapid...

Fraud detection in financial services is a real-time AI workload. The system must ingest transaction data, retrieve historical customer profiles, and perform inference in milliseconds. This requires a storage architecture that supportsmulti-protocol access(such as S3 for ingestion and POSIX/NFS for inference engines) combined withlow latency. In these environments, storage latency directly impacts the "Time to Decision". An All-Flash storage tier is mandatory, as traditional HDD solutions (Option D) or moderate speed systems (Option B) introduce "Tail Latency" that can cause the fraud detection model to time out during peak transaction windows. Additionally, multi-site synchronization ensures that the latest model weights and historical data are available across different geographic data centers for high availability and localized inference.