Components of an AWS Config Rule - AWS Config
Rule metadataRule structure

Components of an AWS Config Rule

AWS Config rules evaluate the configuration settings of your AWS resources. There are two types of rules: AWS Config Managed Rules and AWS Config Custom Rules.

AWS Config Managed Rules are predefined, customizable rules created by AWS Config. For a list of managed rules, see List of AWS Config Managed Rules.

AWS Config Custom Rules are rules that you create from scratch. There are two ways to create AWS Config custom rules: with Lambda functions (AWS Lambda Developer Guide) and with Guard (Guard GitHub Repository), a policy-as-code language. AWS Config custom rules created with AWS Lambda are called AWS Config Custom Lambda Rules and AWS Config custom rules created with Guard are called AWS Config Custom Policy Rules.

This page discusses the metadata of AWS Config managed rules and best practices on how to write AWS Config custom rules with Python using the AWS Config Rules Development Kit (RDK) and AWS Config Rules Development Kit Library (RDKlib). For a walkthrough showing how to create AWS Config Custom Policy Rules, see Creating AWS Config Custom Policy Rules. For a walkthrough showing how to create AWS Config Custom Lambda Rules, see Creating AWS Config Custom Lambda Rules.

AWS Config managed rule metadata

AWS Config managed rules can contain the following mutable metadata:

defaultName

The defaultName is the name that instances of a rule will get by default.

description

The rule description provides context for what the rule evaluates. The AWS Config Console has a limit of 256 characters. As a best practice, the rule description should begin with “Checks if” and include a description of the NON_COMPLIANT scenario. Service Names should be written in full beginning with AWS or Amazon when first mentioned in the rule description. For example, AWS CloudTrail or Amazon CloudWatch instead of CloudTrail or CloudWatch for first use. Services names can be abbreviated after subsequent reference.

scope

The scope determines which resource types the rule targets. For a list of supported resource types, see Supported Resource Types.

compulsoryInputParameterDetails

The compulsoryInputParameterDetails are used for parameters that are required for a rule to do its evaluation. For example, the access-keys-rotated managed rule includes maxAccessKeyAge as a required parameter. If a parameter is required, it will not be marked as (Optional). For each parameter, a type must be specified. Type can be one of "String", "int", "double", "CSV", "boolean" and "StringMap".

optionalInputParameterDetails

The optionalInputParameterDetails are used for parameters that are optional for a rule to do its evaluation. For example, the elasticsearch-logs-to-cloudwatch managed rule includes logTypes as an optional parameter. For each parameter, a type must be specified. Type can be one of "String", "int", "double", "CSV", "boolean" and "StringMap".

supportedEvaluationModes

The supportedEvaluationModes determines when resources will be evaluated, either before a resource has been deployed or after a resource has been deployed.

DETECTIVE is used to evaluate resources which have already been deployed. This allows you to evaluate the configuration settings of your existing resources. PROACTIVE is used to evaluate resources before they have been deployed.

This allows you to evaluate whether a set of resource properties, if used to define an AWS resource, would be COMPLIANT or NON_COMPLIANT given the set of proactive rules that you have in your account in your Region.

You can specify the supportedEvaluationModes to DETECTIVE, PROACTIVE, or both DETECTIVE and PROACTIVE. You must specify an evaluation mode and this field cannot remain empty.

For more information, see Evaluation modes. For a list of managed rules that support proactive evaluation, see List of AWS Config Managed Rules by Evaluation Mode.

Note

Proactive rules do not remediate resources that are flagged as NON_COMPLIANT or prevent them from being deployed.

AWS Config custom rule structure

This section contains information on using the AWS Config Rules Development Kit (RDK) and AWS Config Rules Development Kit Library (RDKlib). For more information on the RDK or RDKlib, see the aws-config-rdk and aws-config-rdklib GitHub Repositories.

Writing rules

  1. Follow the steps in Installing the AWS CLI.

  2. Follow the steps in Setting Up AWS Config with the Console or Setting Up AWS Config with the AWS CLI. For information about the AWS Regions where AWS Config is supported, select your Region from the AWS Regional Services list.

  3. Install the RDK, using the recommended method with pip:

    pip install rdk
    Note

    Before using pip, make sure it is installed on your machine.

  4. Install the RDKLib, using the recommended method with pip:

    pip install rdklib
    Note

    Before using pip, make sure it is installed on your machine.

  1. To create a rule that is triggered by a change to specified resource type, run the following command:

    rdk create YOUR_RULE_NAME --runtime python3.6-lib --resource-types AWS::Resource::Type

    The following example creates a rule that is triggered by a change to the AWS::IAM::User resource type:

    rdk create MFA_ENABLED_RULE --runtime python3.6-lib --resource-types AWS::IAM::User

    The following are the flags you can use with the rdk create command for change-triggered rules:

    rdk create RULE_NAME 
    --runtime pythonX.X-lib // Python runtime version
    --input-parameters REQUIRED_PARAMETERS // Parameters that are required for a rule to do its evaluation
    --optional-parameters OPTIONAL_PARAMETERS // Parameters that are optional for a rule to do its evaluation
    --resource-types AWS::Resource::Type // Resource type(s) that the rule targets
    Note

    To use the RDKLib, the runtime of the rule must be set to python3.6-lib.

    After running rdk create, you should see a new directory with the rule name and 3 files in it:

    • RULE_NAME.py - Python file where the rule logic is stored


    • RULE_NAME_test.py - Python file where the rule's unit tests is stored

    • parameters.json - JSON file for RDK's deployment settings

  2. The next step is writing the rule logic. You will only need to edit the RULE_NAME.py file. If you open the RULE_NAME.py file, you will see a template where you can add rule logic. The following is the template that was generated for MFA_ENABLED_RULE:

    from rdklib import Evaluator, Evaluation, ConfigRule, ComplianceType
                            
APPLICABLE_RESOURCES = ['AWS::IAM::User']
                            
class MFA_ENABLED_RULE(ConfigRule):

    def evaluate_change(self, event, client_factory, configuration_item, valid_rule_parameters):
        ###############################
        # Add your custom logic here. #
        ###############################                       
                            
        return [Evaluation(ComplianceType.NOT_APPLICABLE)]
                            
    #def evaluate_periodic(self, event, client_factory, valid_rule_parameters):
    #    pass
                            
    def evaluate_parameters(self, rule_parameters):
        valid_rule_parameters = rule_parameters
        return valid_rule_parameters
                                                        
################################
# DO NOT MODIFY ANYTHING BELOW #
################################
def lambda_handler(event, context):
    my_rule = MFA_ENABLED_RULE()
    evaluator = Evaluator(my_rule, APPLICABLE_RESOURCES)
    return evaluator.handle(event, context)

    The following example is an edited version of the MFA_ENABLED_RULE template with the rule logic. The rule checks if IAM users have multi-factor authentication (MFA) enabled. The rule is NON_COMPLIANT if an IAM user does not have MFA not enabled. For more information on rule logic and the methods provided in the template, see Rule logic.

    from rdklib import ComplianceType, ConfigRule, Evaluation, Evaluator

APPLICABLE_RESOURCES = ["AWS::IAM::User"]

class MFA_ENABLED_RULE(ConfigRule):
        
    def evaluate_change(self, event, client_factory, configuration_item, valid_rule_parameters):
    
        username = configuration_item.get("resourceName")
        
        iam_client = client_factory.build_client("iam")
        
        response = iam_client.list_mfa_devices(UserName=username)
        
        # Scenario:1 IAM user has MFA enabled.
        if response["MFADevices"]:
            return [Evaluation(ComplianceType.COMPLIANT)]
            
        # Scenario:2 IAM user has MFA not enabled.
        annotation = "MFA needs to be enabled for user."
        return [Evaluation(ComplianceType.NON_COMPLIANT, annotation=annotation)]
        
    def evaluate_parameters(self, rule_parameters):
        valid_rule_parameters = rule_parameters
        return valid_rule_parameters

################################
# DO NOT MODIFY ANYTHING BELOW #
################################
def lambda_handler(event, context):
    my_rule = MFA_ENABLED_RULE()
    evaluator = Evaluator(my_rule, APPLICABLE_RESOURCES)
    return evaluator.handle(event, context)

  3. The next step is installing the RDKlib layer in AWS with either the AWS Console or AWS CLI. RDKLib is designed to work as an AWS Lambda Layer. It allows you to use the library without needing to include it in your deployment package.

    • To install the RDKlib layer with the AWS Console, do the following steps:

      1. Open the AWS Lambda console at https://console.aws.amazon.com/lambda/.

      2. Select Create function.

      3. On the Create function page, select Browse serverless app repository, and in the search field, enter rdklib.

      4. Review the function details and then deploy it. You shouldn't have to make any changes.

      5. On the left navigation pane, choose the Layers page. Then choose the Lambda layer you just created, and copy the Amazon Resource Name (ARN) of the Lambda layer. You will need the ARN of the Lambda layer when you deploy your rule.

    • To install the RDKlib layer with the AWS CLI, run the following commands:

      1. Create the change set for the RDKlib-Layer.

        aws serverlessrepo create-cloud-formation-change-set --application-id arn:aws:serverlessrepo:ap-southeast-1:711761543063:applications/rdklib --stack-name RDKlib-Layer

        It returns the following output:

        {
    "ApplicationId": "arn:aws:serverlessrepo:ap-southeast-1:711761543063:applications/rdklib",
    "ChangeSetId": "arn:aws:cloudformation:us-east-1:123456789012:changeSet/a3d536322-585e-4ffd-9e2f-552c8b887d6f/ffe7ff5c-ab38-4ab9-b746-9c1617ca95c1",
    "SemanticVersion": "0.1.0",
    "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/serverlessrepo-RDKlib-Layer/365436a0-a58a-11ea-9b04-12ae8fb95b53"
}

      2. Execute the change-set. You can copy/paste the full change-set ARN (ChangeSetId from the output generated in the previous step) to customize the following command:

        aws cloudformation execute-change-set --change-set-name NAME_OF_THE_CHANGE_SET

      3. Return all the associated resources that are part of the deployed stack.

        aws cloudformation describe-stack-resources --stack-name serverlessrepo-RDKlib-Layer

        It returns the following output:

        {
    "StackResources": [
        {
            "StackName": "serverlessrepo-RDKlib-Layer",
            "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/serverlessrepo-RDKlib-Layer/365436a0-a58a-11ea-9b04-12ae8fb95b53",
            "LogicalResourceId": "RdklibLayercf22317faf",
            "PhysicalResourceId": "arn:aws:lambda:us-east-1:123456789012:layer:rdklib-layer:1",
            "ResourceType": "AWS::Lambda::LayerVersion",
            "Timestamp": "2020-06-03T11:26:30.501Z",
            "ResourceStatus": "CREATE_COMPLETE",
            "DriftInformation": {
                "StackResourceDriftStatus": "NOT_CHECKED"
            }
        }
    ]
}

      4. Copy the ARN of the Lambda layer from the output generated in the previous step. The ARN of the Lambda layer is the PhysicalResourceId.

        "PhysicalResourceId": "arn:aws:lambda:us-east-1:123456789012:layer:rdklib-layer:1"

  4. The next step is providing a role for the Lambda function to assume. By default, Lambda functions attempt to assume the AWSServiceRoleForConfig role, which is not allowed. You need to create a role with the AWS_ConfigRole managed policy. The role must have a trust relationship with AWS Config and all roles under the /rdk/ path should assume the role. The following is an example trust policy:

    {
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "config.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    },
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::account-ID:root"
      },
      "Action": "sts:AssumeRole",
      "Condition": {
        "StringLike": {
          "aws:PrincipalArn": "arn:aws:iam::account-ID:role/rdk/*"
        }
      }
    }
  ]
}

    Using this trust policy, run the following command:

    aws iam create-role --role-name your-role-name --assume-role-policy-document file://trust-policy.json

    Now, run the following command to update the input parameter for ExecutionRoleName and provide the role name:

    rdk modify YOUR_RULE_NAME --input-parameters '{"ExecutionRoleName":"your-role-name"}'

    You can also use rdk modify to update the change-triggered rule details using the following flags:

    rdk modify RULE_NAME 
    --input-parameters REQUIRED_PARAMETERS // Parameters that are required for a rule to do its evaluation
    --optional-parameters OPTIONAL_PARAMETERS // Parameters that are optional for a rule to do its evaluation
    --resource-types AWS::Resource::Type // Resource type(s) that the rule targets

  5. The final step is deploying your rule. To deploy your rule, run the following command with the ARN of the Lambda layer from step 3:

    rdk deploy YOUR_RULE_NAME --rdklib-layer-arn YOUR_RDKLIB_LAYER_ARN

  6. The rule is now deployed. You can use the AWS Config Console to check if the rule is working as expected.

  1. To create a rule that is triggered periodically for a specified resource type, run the following command:

    rdk create YOUR_RULE_NAME --runtime python3.6-lib --resource-types AWS::Resource::Type --maximum-frequency EXECUTION_FREQUENCY

    The following example creates a rule that is triggered every 24 hours for the AWS::IAM::User resource type:

    rdk create MFA_ENABLED_RULE --runtime python3.6-lib --resource-types AWS::IAM::User --maximum-frequency TwentyFour_Hours

    The following are the flags you can use with the rdk create command for periodic rules:

    rdk create RULE_NAME 
    --runtime pythonX.X-lib // Python runtime version
    --input-parameters REQUIRED_PARAMETERS // Parameters that are required for a rule to do its evaluation
    --optional-parameters OPTIONAL_PARAMETERS // Parameters that are optional for a rule to do its evaluation
    --resource-types AWS::Resource::Type // Resource type(s) that the rule targets
    --maximum-frequency EXECUTION_FREQUENCY // How often the rule should be run on a periodic trigger.
 One of ['One_Hour','Three_Hours','Six_Hours','Twelve_Hours','TwentyFour_Hours']
    Note

    To use the RDKLib, the runtime of the rule must be set to python3.6-lib.

    After running rdk create, you should see a new directory with the rule name and 3 files in it:

    • RULE_NAME.py - Python file where the rule logic is stored


    • RULE_NAME_test.py - Python file where the rule's unit tests is stored

    • parameters.json - JSON file for RDK's deployment settings

  2. The next step is writing the rule logic. You will only need to edit the RULE_NAME.py file. If you open the RULE_NAME.py file, you will see a template where you can add rule logic. The following is the template that was generated for MFA_ENABLED_RULE:

    from rdklib import Evaluator, Evaluation, ConfigRule, ComplianceType
                            
APPLICABLE_RESOURCES = ['AWS::IAM::User']
                            
class MFA_ENABLED_RULE(ConfigRule):

    def evaluate_change(self, event, client_factory, configuration_item, valid_rule_parameters):
        ###############################
        # Add your custom logic here. #
        ###############################                       
                            
        return [Evaluation(ComplianceType.NOT_APPLICABLE)]
                            
    #def evaluate_periodic(self, event, client_factory, valid_rule_parameters):
    #    pass
                            
    def evaluate_parameters(self, rule_parameters):
        valid_rule_parameters = rule_parameters
        return valid_rule_parameters
                                                        
################################
# DO NOT MODIFY ANYTHING BELOW #
################################
def lambda_handler(event, context):
    my_rule = MFA_ENABLED_RULE()
    evaluator = Evaluator(my_rule, APPLICABLE_RESOURCES)
    return evaluator.handle(event, context)

    The template defaults to change-triggered rules. Instead, add your logic to the evaluate_periodic method. The following example is an edited version of the MFA_ENABLED_RULE template with the rule logic. The rule checks if IAM users have multi-factor authentication (MFA) enabled. The rule is NON_COMPLIANT if an IAM user does not have MFA not enabled. For more information on rule logic and the methods provided in the template, see Rule logic.

    from rdklib import ComplianceType, ConfigRule, Evaluation, Evaluator

APPLICABLE_RESOURCES = ["AWS::IAM::User"]

class MFA_ENABLED_RULE(ConfigRule):l
        
    def evaluate_periodic(self, event, client_factory, valid_rule_parameters):
        evaluations = []
        
        iam_client = client_factory.build_client("iam")
        
        paginator = iam_client.get_paginator("list_users")
        response_iterator = paginator.paginate()
        
        for response in response_iterator:
            for user in response["Users"]:
                username = user["UserName"]
                response = iam_client.list_mfa_devices(UserName=username)
                
                # Scenario:1 IAM user has MFA enabled.
                if response["MFADevices"]:
                    evaluations.append(Evaluation(ComplianceType.COMPLIANT, username, "AWS::IAM::User"))
                    
                # Scenario:2 IAM user has MFA not enabled.
                if not response["MFADevices"]:
                    annotation = "MFA needs to be enabled for user."
                    evaluations.append(
                        Evaluation(ComplianceType.NON_COMPLIANT, username, "AWS::IAM::User", annotation=annotation)
                    )                  
        return evaluations
    
    def evaluate_parameters(self, rule_parameters):
        valid_rule_parameters = rule_parameters
        return valid_rule_parameters

################################
# DO NOT MODIFY ANYTHING BELOW #
################################
def lambda_handler(event, context):
    my_rule = MFA_ENABLED_RULE()
    evaluator = Evaluator(my_rule, APPLICABLE_RESOURCES)
    return evaluator.handle(event, context)

  3. The next step is installing the RDKlib layer in AWS with either the AWS Console or AWS CLI. RDKLib is designed to work as an AWS Lambda Layer. It allows you to use the library without needing to include it in your deployment package.

    • To install the RDKlib layer with the AWS Console, do the following steps:

      1. Open the AWS Lambda console at https://console.aws.amazon.com/lambda/.

      2. Select Create function.

      3. On the Create function page, select Browse serverless app repository, and in the search field, enter rdklib.

      4. Review the function details and then deploy it. You shouldn't have to make any changes.

      5. On the left navigation pane, choose the Layers page. Then choose the Lambda layer you just created, and copy the Amazon Resource Name (ARN) of the Lambda layer. You will need the ARN of the Lambda layer when you deploy your rule.

    • To install the RDKlib layer with the AWS CLI, run the following commands:

      1. Create the change set for the RDKlib-Layer.

        aws serverlessrepo create-cloud-formation-change-set --application-id arn:aws:serverlessrepo:ap-southeast-1:711761543063:applications/rdklib --stack-name RDKlib-Layer

        It returns the following output:

        {
    "ApplicationId": "arn:aws:serverlessrepo:ap-southeast-1:711761543063:applications/rdklib",
    "ChangeSetId": "arn:aws:cloudformation:us-east-1:123456789012:changeSet/a3d536322-585e-4ffd-9e2f-552c8b887d6f/ffe7ff5c-ab38-4ab9-b746-9c1617ca95c1",
    "SemanticVersion": "0.1.0",
    "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/serverlessrepo-RDKlib-Layer/365436a0-a58a-11ea-9b04-12ae8fb95b53"
}

      2. Execute the change-set. You can copy/paste the full change-set ARN (ChangeSetId from the output generated in the previous step) to customize the following command:

        aws cloudformation execute-change-set --change-set-name NAME_OF_THE_CHANGE_SET

      3. Return all the associated resources that are part of the deployed stack.

        aws cloudformation describe-stack-resources --stack-name serverlessrepo-RDKlib-Layer

        It returns the following output:

        {
    "StackResources": [
        {
            "StackName": "serverlessrepo-RDKlib-Layer",
            "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/serverlessrepo-RDKlib-Layer/365436a0-a58a-11ea-9b04-12ae8fb95b53",
            "LogicalResourceId": "RdklibLayercf22317faf",
            "PhysicalResourceId": "arn:aws:lambda:us-east-1:123456789012:layer:rdklib-layer:1",
            "ResourceType": "AWS::Lambda::LayerVersion",
            "Timestamp": "2020-06-03T11:26:30.501Z",
            "ResourceStatus": "CREATE_COMPLETE",
            "DriftInformation": {
                "StackResourceDriftStatus": "NOT_CHECKED"
            }
        }
    ]
}

      4. Copy the ARN of the Lambda layer from the output generated in the previous step. The ARN of the Lambda layer is the PhysicalResourceId.

        "PhysicalResourceId": "arn:aws:lambda:us-east-1:123456789012:layer:rdklib-layer:1"

  4. The next step is providing a role for the Lambda function to assume. By default, Lambda functions attempt to assume the AWSServiceRoleForConfig role, which is not allowed. You need to create a role with the AWS_ConfigRole managed policy. The role must have a trust relationship with AWS Config and all roles under the /rdk/ path should assume the role. The following is an example trust policy:

    {
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "config.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    },
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::account-ID:root"
      },
      "Action": "sts:AssumeRole",
      "Condition": {
        "StringLike": {
          "aws:PrincipalArn": "arn:aws:iam::account-ID:role/rdk/*"
        }
      }
    }
  ]
}

    Using this trust policy, run the following command:

    aws iam create-role --role-name your-role-name --assume-role-policy-document file://trust-policy.json

    Now, run the following command to update the input parameter for ExecutionRoleName and provide the role name:

    rdk modify YOUR_RULE_NAME --input-parameters '{"ExecutionRoleName":"your-role-name"}'

    You can also use rdk modify to update the periodic rule details using the following flags:

    rdk modify RULE_NAME 
    --input-parameters REQUIRED_PARAMETERS // Parameters that are required for a rule to do its evaluation
    --optional-parameters OPTIONAL_PARAMETERS // Parameters that are optional for a rule to do its evaluation
    --resource-types AWS::Resource::Type // Resource type(s) that the rule targets
    --maximum-frequency EXECUTION_FREQUENCY // How often the rule should be run on a periodic trigger.
 One of ['One_Hour','Three_Hours','Six_Hours','Twelve_Hours','TwentyFour_Hours']

  5. The final step is deploying your rule. To deploy your rule, run the following command with the ARN of the Lambda layer from step 3:

    rdk deploy YOUR_RULE_NAME --rdklib-layer-arn YOUR_RDKLIB_LAYER_ARN

  6. The rule is now deployed. You can use the AWS Config Console to check if the rule is working as expected.

Rule logic

The following Python code sample is a template for writing a rule using the RDK and RDKLib. You should only make changes inside the evaluate_parameters, evaluate_change, and evaluate_periodic methods, or write completely new functions to help with the logic if needed. For prerequistes to writing rules with the RDK and RDKlib, see Prerequisites.

from rdklib import Evaluator, Evaluation, ConfigRule, ComplianceType

APPLICABLE_RESOURCES = ["AWS::Resource::Type"]

# When you create a rule, the class name will be the name you give the rule when you create it instead of ConfigRule
class ConfigRule (ConfigRule):

    def evaluate_parameters(self, rule_parameters):
        return rule_parameters

    def evaluate_change(self, event, client_factory, configuration_item, valid_rule_parameters):
        ###############################
        # Add your custom logic here. #
        ###############################

    def evaluate_periodic(self, event, client_factory, valid_rule_parameters):
        ###############################
        # Add your custom logic here. #
        ###############################
    
################################
# DO NOT MODIFY ANYTHING BELOW #
################################    
def lambda_handler(event, context):
    my_rule = ConfigRule()
    evaluator = Evaluator(my_rule, APPLICABLE_RESOURCES)
    return evaluator.handle(event, context)
APPLICABLE_RESOURCES

APPLICABLE_RESOURCES are the resource type(s) that the rule targets. If used, this should be a global variable set to the resource type(s) that the rule targets. For a list of supported resource types, see Supported Resource Types.

evaluate_parameters

Description

This method is used to check if the input parameters for the rule are valid. The following are best practices:

  • Check if the correct number of parameters is listed.

  • Check if the parameter name is correct.

  • Check if the parameter value is of the correct type.

  • If the parameter is an integer, check if the parameter is between a reasonable bounds.

  • If the parameter has a limited number of possible options, check if the parameter is one of those options.

  • If the parameter is a String, check if it is a reasonable length and trim any space before or after the value.

  • Check if any case-sensitivity is handled appropriately.

  • Limit parameter input when possible. For example, if you're receiving a comma-separated list of ARNs, make sure that the only characters allowed are commas and the characters supported by ARNs.

Parameters

rule_parameters is a dictionary of input parameter(s) for the rule.

Return syntax

If one of the parameters is not valid, you can raise an InvalidParametersError error:

from rdklib import InvalidParametersError
raise InvalidParametersError("Error message to display")

If the parameters are all valid, the method should return a dictionary:

return valid_rule_parameters
evaluate_change

Description

This method is used for the logic to evaluate a change-triggered rule.

Parameters

event is the AWS Lambda event provided by AWS Config. It is a JSON-formatted document that contains data for a Lambda function to operate. For examples, see Example Events for AWS Config Rules.

client_factory is the ClientFactory object to be used for the rule. The ClientFactory class creates or reuses a boto3 client, which provides low-level interface to an AWS service. The boto3 client methods map with an AWS service API, which means that service operations map to client methods of the same name and provide access to the same operation parameters. For a list of available services, see Available services in the Boto3 Docs documentation.

The request syntax of client_factory is as follows:

response = client_factory.build_client(
    service='string')

For example: 

iam_client = client_factory.build_client("iam")
Note

The boto3 name of the AWS service is required.

configuration_item is dictionary of the full configuration Item, even if oversized. A configuration item represents a point-in-time view of the various attributes of a supported AWS resource. For information on the contents of ConfigurationItem, see ConfigurationItem in the AWS Config API Reference.

valid_rule_parameters is the output of the evaluate_parameters() method.

Return syntax

The method should return one or more of the following:

[Evaluation(ComplianceType.COMPLIANT)]
[Evaluation(ComplianceType.NON_COMPLIANT)]
[Evaluation(ComplianceType.NOT_APPLICABLE)]
Note

Rules reporting on deleted resources should return the evaluation result of NOT_APPLICABLE in order to avoid unnecessary rule evaluations.

You should use annotations for all non-compliant evaluations. For example:

[return [Evaluation(ComplianceType.NON_COMPLIANT, annotation="Explanation for why the rule is NON_COMPLIANT")]]
evaluate_periodic

Description

This method is used to evaluate a periodic rule.

Parameters

event is the AWS Lambda event provided by AWS Config. It is a JSON-formatted document that contains data for a Lambda function to operate. For examples, see Example Events for AWS Config Rules.

client_factory is the ClientFactory object to be used for the rule. The ClientFactory class creates or reuses a boto3 client, which provides low-level interface to an AWS service. The boto3 client methods map with an AWS service API, which means that service operations map to client methods of the same name and provide access to the same operation parameters. For a list of available services, see Available services in the Boto3 Docs documentation.

The request syntax of client_factory is as follows:

response = client_factory.build_client(
    service='string')

For example: 

iam_client = client_factory.build_client("iam")
Note

The boto3 name of the AWS service is required.

valid_rule_parameters is the output of the evaluate_parameters() method.

Return syntax

The method should return one or more of the following:

[Evaluation(ComplianceType.COMPLIANT)]
[Evaluation(ComplianceType.NON_COMPLIANT)]
[Evaluation(ComplianceType.NOT_APPLICABLE)]
Note

Rules reporting on deleted resources should return the evaluation result of NOT_APPLICABLE in order to avoid unnecessary rule evaluations.

You should use annotations for all non-compliant evaluations. For example:

[return [Evaluation(ComplianceType.NON_COMPLIANT, annotation="Explanation for why the rule is NON_COMPLIANT")]]
lambda_handler

Description

You should not need to modify this method. The lambda handler is used to processes events. The function runs when AWS Lambda passes the event object to the handler method. For more information, see Lambda function handler in Python.

Parameters

event is the AWS Lambda event provided by AWS Config. It is a JSON-formatted document that contains data for a Lambda function to operate. For examples, see Example Events for AWS Config Rules.

context is an object is passed to your function by Lambda at runtime. This object provides methods and properties that provide information and methods that the function can use while it runs. Note that in newer versions of Lambda, context is no longer used.