AI SECURITY & RELIABILITY - AWS ARCHITECTURE
AI Output Quality &
Hallucination Firewall
An evidence-backed verification layer between an LLM and the end user. It checks generated claims against trusted sources, calculates measurable support signals, and applies a PASS, WARN, or REJECT policy before the answer is trusted.
PRIMARY FOCUSAI Reliability & Hallucination Mitigation
PLATFORMAmazon Bedrock + Serverless AWS
DESIGN PATTERNGenerate - Retrieve - Verify - Decide
AI OUTPUT FIREWALL
Verify - Validate - Trust - AWS Serverless Reference Architecture
USERWeb / App
Question or answer
Question or answer
>
API GATEWAYHTTPS /evaluate
Public API boundary
Public API boundary
>
AWS LAMBDAGenerate - Retrieve
Verify - Score - Audit
Verify - Score - Audit
>
AMAZON BEDROCKCandidate generation
Grounded retrieval
Grounded retrieval
>
VERIFICATIONClaim extraction
Evidence scoring
PASS / WARN / REJECT
Evidence scoring
PASS / WARN / REJECT
TRUSTED S3Curated sources
KNOWLEDGE BASERAG retrieval
DYNAMODBAudit trail
CLOUDWATCHLogs and metrics
RELIABLE, EVIDENCE-GROUNDED RESPONSE + CONFIDENCE + SOURCES
AWS Service Symbology View
AWS serverless reference architecture showing generation, trusted-source retrieval, evidence verification, and PASS / WARN / REJECT decisions. Select the drawing to view it full size.
Technology Stack
Amazon BedrockAPI GatewayAWS LambdaAmazon S3Knowledge BasesDynamoDBCloudWatchPython
Security & Quality Controls
Generation and verification are separate trust stages.
Claims are checked against authoritative evidence.
Evidence provenance supports review and auditability.
DynamoDB and CloudWatch provide operational visibility.
MICROSOFT AZURE ARCHITECTURE DIAGRAM
Azure reference equivalent for portfolio comparison. The original project implementation remains as documented elsewhere on this page.