A bank is fine-tuning an LLM on Amazon Bedrock to answer customer loan questions. To ensure the model does not expose private customer information, which action should the bank take?

Removing PII from the customer data before fine-tuning is crucial because fine-tuning directly incorporates the training data's patterns and content into the model. If PII is present during fine-tuning, the LLM could inadvertently generate or reveal that sensitive information in its responses, even if not explicitly prompted to do so. This proactive data sanitization prevents the model from ever learning or storing the PII. Amazon Bedrock Guardrails help control the LLM's output after it has been fine-tuned, by filtering harmful or off-topic content, but they cannot remove PII that has already been embedded in the model's weights during training. Increasing the Top-K parameter affects the diversity of generated text but does not prevent the generation of PII if it was learned. Storing data in Amazon S3 and encrypting it are good security practices for data at rest and in transit, but they do not prevent the PII from being learned by the LLM during the fine-tuning process itself.