AWS Config Rules

AWS Config Rules address the critical challenge of ensuring compliance and configuration consistency across large and complex AWS environments. Here’s a technical overview of how they operate:

AWS Config Rules leverage AWS Lambda functions to evaluate the configuration settings of AWS resources. These rules can be triggered by configuration changes or run periodically. The rules are developed using the AWS Config Rules Development Kit (RDK), which streamlines the process of setting up, authoring, and testing custom rules. The RDK abstracts away much of the complexity associated with deploying these rules, enabling a "Compliance-as-Code" workflow.

To deploy a custom Config rule, you create an AWS Lambda function based on sample code provided in the repository. This function is then associated with a Config rule through the AWS Config console. The rule parameters and trigger types (configuration changes or periodic) must be carefully specified to ensure the rule functions as intended. For example, if using a periodic trigger, you need to configure the appropriate schedule and any required parameters.

When a rule is triggered, AWS Config invokes the associated Lambda function, passing relevant configuration items and event data. The function evaluates the compliance status and returns a result (COMPLIANT, NON_COMPLIANT, or NOT_APPLICABLE) to AWS Config. The RDK provides tools to generate CloudFormation templates for deploying these rules across multiple accounts, including satellite accounts, using StackSets or CodePipeline.

The evaluation results are displayed on the AWS Config console, providing insights into the compliance status of your AWS resources. The rules can be managed and operated at scale using the AWS Config Engine for Compliance-as-Code, which automates the deployment and operation of Config rules via preconfigured pipelines. This engine also provides centralized management and visibility through the AWS Config Rules Dashboard.

While AWS Config Rules offer robust compliance monitoring, they do come with operational limits. For instance, the number of rules that can be deployed and the frequency of evaluations can impact performance. Additionally, managing permissions for Lambda functions and ensuring the correct configuration of rule parameters are crucial to avoid errors and ensure smooth operation.