Checklist for Complete Startup AI Governance

 Checklist for Complete Startup AI Governance

​To ensure your application is watertight, configure these four layers inside your Amazon Bedrock Guardrail policy:

​Content Filters: Block or mask inputs/outputs based on explicit categories (Hate Speech, Insults, Sexual Content, Violence, Misconduct). You can adjust the sensitivity slider from Low to High.  

​Prompt Attack / Jailbreak Detection: Enable protection against user prompts designed to bypass your system prompts (like prompt injections or "jailbreaks").  

​Sensitive Information (PII) Filters: Add rules to automatically mask or entirely block data elements like Social Security Numbers, credit card numbers, email addresses, or custom regular expressions (like internal database IDs).  

​Contextual Grounding (Anti-Hallucination): Essential for RAG architectures. This automatically calculates a validation score checking how much of the LLM's answer is actually found in your source document. If the model starts hallucinating facts not found in your S3 data, the guardrail blocks the response.

​💡 The Startup Pro-Tip

​Start by building one Master Guardrail in the AWS console with your company's baseline compliance message (e.g., "I'm sorry, but that request violates our data security policies."). Use Method 1 in your chat APIs for seamless, low-latency defense, and implement Method 2 if you build any background pipelines where you need to pre-scrub large batches of input text.