Question

A healthcare startup uses Amazon Bedrock with Anthropic Claude to power a patient-facing chatbot. Compliance requires that any Social Security numbers or phone numbers entered by users are never sent to the model or stored in logs, and that the model refuses to produce medical dosage recommendations. The team wants a managed, model-agnostic mechanism that enforces these rules on both input prompts and model output without writing custom regex-based filtering code in the application layer. Which approach best meets these requirements?

Accepted Answer

Configure an Amazon Bedrock Guardrail with sensitive information filters (PII masking/blocking) plus denied topics, and attach the guardrail to the InvokeModel calls.

Answer

Enable Amazon Bedrock model invocation logging to CloudWatch and use a Lambda subscription filter to scrub PII after each request completes.

Answer

Use Amazon Comprehend PII detection in the application before calling Bedrock, and rely on the model's system prompt to refuse dosage questions.

Answer

Fine-tune the Claude model on a redacted dataset so it learns to ignore SSNs and phone numbers and never produce dosage recommendations.