c1.9 Validate model outputs before downstream use
Never pass raw model output to downstream systems or users unchecked; validate and ground responses to reduce hallucinated or malicious content taking effect.
Implementation guidance
How to implement on AWS
Use Bedrock Guardrails contextual grounding and, where applicable, Automated Reasoning checks to verify responses against approved sources. Strip internal metadata/system-prompt content from responses, and validate output structure before downstream consumption.
Acceptance criteria
Outputs are grounded/validated before use; a fabricated answer is caught; responses contain no internal system metadata.
Practical implementation
How this control is implemented in each reference architecture:
Grounding and output validation run in the Generative AI account before the response is returned to the Application account for downstream use.
- Enable Bedrock Guardrails contextual grounding (and Automated Reasoning checks where applicable) to verify responses against approved sources in the Generative AI account.Generative AI account
Amazon Bedrock - Strip internal metadata and system-prompt content from responses, and validate output structure (schema/type) with a Lambda step before returning to the Application account.Generative AI account → Application account
AWS Lambda - In the Application account, treat model output as untrusted: never pass it directly to a shell, query, or privileged action without validation, and validate request/response schema at API Gateway.Application account AWS Lambda
API Gateway
How to validate: A fabricated (ungrounded) answer is caught; responses carry no internal system metadata; downstream actions run only on validated output.
Avoid: Feeding raw model output straight into a database query or automation. Ground it, validate its shape, and treat it as untrusted input.
The generative AI tier grounds and validates output before the application tier consumes it — model output is never trusted blindly.
- Turn on contextual grounding checks (and Automated Reasoning where applicable) on the guardrail in the generative AI tier.Generative AI tier (IAM role) Amazon Bedrock
- Add a validation step (Lambda) that strips system/internal content and checks output structure before the application tier uses it.Application tier (IAM role) AWS Lambda
- Ensure any downstream action (query, tool call) runs only on validated, schema-checked output, with request/response schema enforced at API Gateway.Application tier (IAM role) AWS Lambda API Gateway
How to validate: An ungrounded answer is flagged; responses contain no internal metadata; downstream use consumes only validated output.
Avoid: Rendering or executing model output as-is. Validate structure and ground content first, especially before any automated action.
Threats addressed
- Unvalidated outputs triggering flawed automated actions.
- Misinformation or improperly handled output reaching users or systems.
AWS services
Amazon Bedrock Guardrails Amazon API Gateway Framework mappings
OWASP GenAI/LLM Top 10 (2026)
- LLM07 Misinformation
- LLM10 Improper Output Handling
NIST AI Risk Management Framework (AI 100-1)
- MEASURE.2 AI systems are evaluated for trustworthy characteristics
- MANAGE.1 AI risks are prioritized, responded to and managed
AWS Well-Architected Generative AI Lens (Security)
- GENSEC02-BP01 Implement guardrails to mitigate harmful or incorrect model responses
Learning resources
References
- AWS AI Security Framework — output validation