Rule Development Kit - Version 2 This tool should be considered in "Open Beta". I would not recommend relying on it for critical workloads, and would greatly appreciate feedback and bug reports at [email protected]!

The RDK is designed to support a "Compliance-as-Code" workflow that is intuitive and productive. It abstracts away much of the undifferentiated heavy lifting associated with deploying AWS Config rules backed by custom lambda functions, and provides a streamlined develop-deploy-monitor iterative process.

Uses python 2.7/3.6 and is installed via pip. Requires you to have an AWS account and sufficient permissions to manage the Config service, and to create S3 Buckets, Roles, and Lambda Functions. An AWS IAM Policy Document that describes the minimum necessary permissions can be found at policy/rdk-minimum-permissions.json.

Under the hood, rdk uses boto3 to make API calls to AWS, so you can set your credentials any way that boto3 recognizes (options 3 through 8 here: https://boto3.readthedocs.io/en/latest/guide/configuration.html) or pass them in with the command-line parameters --profile, --region, --access-key-id, or --secret-access-key

If you just want to use the RDK, go ahead and install it using pip:

$ pip install rdk

Alternately, if you want to see the code and/or contribute you can clone the git repo, and then from the repo directory use pip to install the package. Use the '-e' flag to generate symlinks so that any edits you make will be reflected when you run the installed package.

If you are going to author your Lambda functions using Java you will need to have Java 8 and gradle installed. If you are going to author your Lambda functions in C# you will need to have the dotnet CLI and the .NET Core Runtime 1.08 installed.

$ pip install -e .

To make sure the rdk is installed correctly, running the package from the command line without any arguments should display help information.

$ rdk
usage: rdk [-h] [-p PROFILE] [-k ACCESS_KEY] [-s SECRET_ACCESS_KEY]
         [-r REGION]
         <command> ...
rdk: error: the following arguments are required: <command>, <command arguments>

To use the RDK, create a directory that will be your working directory. This should be committed to a source code repo, and ideally created as a python virtualenv. In that directory, run the init command to set up your AWS Config environment and copy template files into a .rdk directory in your working directory.

$ rdk init
Running init!
Creating Config bucket config-bucket-780784666283
Creating IAM role config-role
Waiting for IAM role to propagate
Config Service is ON
Config setup complete.
Creating Code bucket config-rule-code-bucket-780784666283ap-southeast-1

Running init subsequent times will validate your AWS Config setup and re-create your .rdk directory from whatever version of rdk is installed.

In your working directory, use the create command to start creating a new custom rule. You must specify the runtime for the lambda function that will back the Rule, and you must also specify a resource type (or comma-separated list of types) that the Rule will evaluate. This will add a new directory for the rule and populate it with several files, including a skeleton of your Lambda code.

$ rdk create MyRule --runtime python3.6 --resource-types AWS::EC2::Instance --maximum-frequency One_Hour --input-parameters '{"desiredInstanceType":"t2.micro"}'
Running create!
Local Rule files created.

Once you have created the rule, edit the python file in your rule directory (in the above example it would be MyRule/MyRule.py, but may be deeper into the rule directory tree depending on your chosen Lambda runtime) to add whatever logic your Rule requires in the evaluate_compliance function. You will have access to the CI that was sent by Config, as well as any parameters configured for the Config Rule. Your function should return either COMPLIANT, NONCOMPLIANT, or NOT_APPLICABLE.

If you want to see what the JSON structure of a CI looks like for creating your logic, you can use

$ rdk sample-ci <Resource Type>

to output a formatted JSON document.

If you need to change the parameters of a Config rule in your working directory you can use the modify command. Any parameters you specify will overwrite existing values, any that you do not specify will not be changed.

$ rdk modify MyRule --runtime python2.7 --periodic TwentyFour_Hours --input-parameters '{"desiredInstanceType":"t2.micro"}'
Running modify!
Modified Rule 'MyRule'.  Use the `deploy` command to push your changes to AWS.

It is worth noting that until you actually call the deploy command your rule only exists in your working directory, none of the Rule commands discussed thus far actually makes changes to your account.

Once you have completed your compliance validation code and set your Rule's configuration, you can deploy the Rule to your account using the deploy command. This will zip up your code (and the other associated code files) into a deployable package (or run a gradle build if you have selected the java8 runtime or run the lambda packaging step from the dotnet CLI if you have selected the dotnetcore1.0 runtime), copy that zip file to S3, and then launch or update a CloudFormation stack that defines your Config Rule, Lambda function, and the necessary permissions and IAM Roles for it to function. Since CloudFormation does not deeply inspect Lambda code objects in S3 to construct its changeset, the deploy command will also directly update the Lambda function for any subsequent deployments to make sure code changes are propagated correctly.

$ rdk deploy MyRule
Running deploy!
Zipping MyRule
Uploading MyRule
Creating CloudFormation Stack for MyRule
Waiting for CloudFormation stack operation to complete...
...
Waiting for CloudFormation stack operation to complete...
Config deploy complete.

The exact output will vary depending on Lambda runtime. You can use the --all flag to deploy all of the rules in your working directory.

Once the Rule has been deployed to AWS you can get the CloudWatch logs associated with your lambda function using the logs command.

$ rdk logs MyRule -n 5
2017-11-15 22:59:33 - START RequestId: 96e7639a-ca15-11e7-95a2-b1521890638d Version: $LATEST
2017-11-15 23:41:13 - REPORT RequestId: 68e0304f-ca1b-11e7-b735-81ebae95acda    Duration: 0.50 ms    Billed Duration: 100 ms     Memory Size: 256 MB
                          Max Memory Used: 36 MB
2017-11-15 23:41:13 - END RequestId: 68e0304f-ca1b-11e7-b735-81ebae95acda
2017-11-15 23:41:13 - Default RDK utility class does not yet support Scheduled Notifications.
2017-11-15 23:41:13 - START RequestId: 68e0304f-ca1b-11e7-b735-81ebae95acda Version: $LATEST

You can use the -n and -f command line flags just like the UNIX tail command to view a larger number of log events and to continuously poll for new events. The latter option can be useful in conjunction with manually initiating Config Evaluations for your deploy Config Rule to make sure it is behaving as expected.

No tests yet.

email me at [email protected] if you are interested in contributing.

We use [SemVer](https://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/your/project/tags).

• Greg Kim and Chris Gutierrez - Initial work and CI definitions
• Michael Borchert - Python version
• Henry Huang - CFN templates and other code
• Jonathan Rault - Design, testing, feedback

See also the list of [contributors](https://github.com/your/project/contributors) who participated in this project.

This project is licensed under the Apache 2.0 License - see the [LICENSE.md](LICENSE.md) file for details

• the boto3 team makes all of this magic possible.