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Overview

  • DynamoAI captures application logs and exports them via Fluentd, allowing you to configure any backend logging system of your choice.
  • This document covers the core logging concepts and structure used by DynamoAI components, which are independent of your chosen logging backend.

Log Collection Architecture​

  • DynamoAI uses Fluentd to collect and export logs from all application components
  • In SaaS deployments: Logs are automatically exported to AWS CloudWatch
  • In PaaS deployments: You can configure Fluentd to export logs to any backend of your choice (CloudWatch, Elasticsearch, Splunk, etc.)

Structured Logging​

DynamoAI uses structured logging across its components. Instead of printing logs as plain strings, logs are emitted as JSON objects with consistent attributes. This enables:

  • Easy filtering and querying based on metadata attributes
  • Simplified log exploration and debugging
  • Backend-agnostic log analysis (works with any logging system that supports JSON)

Benefits​

  • Filter logs by specific attributes (e.g., request ID, component, test ID) rather than searching through log text
  • Consistent log structure across all DynamoAI components
  • Better integration with modern logging backends and analysis tools

Component Logging Documentation​

  • DynamoEval Logs: Logging structure for DynamoEval attacks and test report generation
  • Generic Component Logs: Logging structure for API, UI, and MongoDB components
  • DynamoGuard Logs: How to trace logs for DynamoGuard analyze requests from API to Moderation Server
  • CloudWatch Guide: CloudWatch-specific implementation details and query examples (for SaaS deployments or if using CloudWatch as your backend)