Official

AWS Compliance

CloudQuery dbt AWS compliance pack

Publisher

cloudquery

Repository

github.com

Latest version

v1.15.4

Type

Policy

Published

CloudQuery × dbt: AWS Compliance Package

Overview #

Welcome to AWS Compliance Package, a compliance solution that works on top of the CloudQuery framework. This package offers automated checks across various AWS services, following benchmarks such as CIS and AWS foundational security standards. Using this solution you can get instant insights about your security posture and make sure you are following the recommended security guidelines defined by AWS, CIS and more.

We recommend to use this transformation with our AWS Compliance Dashboard

Examples #

How can I check that all my EC2 related resources are following the foundational security standards? (PostgreSQL)

SELECT *
FROM aws_compliance__foundational_security
WHERE check_id LIKE '%ec2.%'

How many checks failed in the CIS 2.0 benchmark? (PostgreSQL)

SELECT count(*) as failed_count
FROM aws_compliance__cis_v2_0_0
WHERE status = 'fail'

Which resource failed the most tests in the foundational security benchmark? (PostgreSQL)

SELECT resource_id, count(*) as failed_count
FROM aws_compliance__foundational_security
WHERE status = 'fail'
GROUP BY resource_id
ORDER BY count(*) DESC

Requirements #

One of the below databases

What's in the pack #

This package includes: Automated compliance checks following CIS, AWS Foundational Security, IMDS V2 and PCI DSS

To run this package you need to complete the following steps #

Setting up the DBT profile (PostgreSQL) #

First, install dbt:

pip install dbt-postgres

Create the profile directory:

mkdir -p ~/.dbt

Create a profiles.yml file in your profile directory (e.g. ~/.dbt/profiles.yml):

aws_compliance: # This should match the name in your dbt_project.yml
  target: dev
  outputs:
    dev:
      type: postgres
      host: 127.0.0.1
      user: postgres
      pass: pass
      port: 5432
      dbname: postgres
      schema: public # default schema where dbt will build the models
      threads: 1 # number of threads to use when running in parallel

Test the Connection:

After setting up your profiles.yml, you should test the connection to ensure everything is configured correctly:

dbt debug

This command will tell you if dbt can successfully connect to your PostgreSQL instance.

Since this policy uses premium features and tables, you must log in to your CloudQuery account by using the command cloudquery login in your terminal.

Migrating Tables #

Before syncing the data, we recommend migrating and creating all the necessary tables to ensure a smoother process flow. Make sure the tables part contains * for the migration.

kind: source
spec:
  name: aws  # The source type, in this case, AWS.
  path: cloudquery/aws  # The plugin path for handling AWS sources.
  registry: cloudquery  # The registry from which the AWS plugin is sourced.
  version: "v27.15.0"  # The version of the AWS plugin.
  tables: ["*"]  # Include all tables for the migration process.
  destinations: ["postgresql"]  # The destination for the data, in this case, PostgreSQL.
  skip_dependent_tables: true
  spec:
    # Additional configuration goes here

Use the command: cloudquery migrate config.yml

Syncing AWS data #

Based on the models you are interested in running, you need to sync the relevant tables. This time, we don’t sync all tables (*), but instead, focus on the relevant tables that match the policy to use fewer resources and save runtime. Below is an example of a sync for the relevant tables for the model AWS CIS V1.2.0 with a PostgreSQL destination. You can modify the list of tables based on the compliance you want to check. This package also supports Snowflake and Google BigQuery.

kind: source
spec:
 name: aws # The source type, in this case, AWS.
 path: cloudquery/aws # The plugin path for handling AWS sources.
 registry: cloudquery # The registry from which the AWS plugin is sourced.
 version: "v27.15.0" # The version of the AWS plugin.
 tables: ["aws_iam_password_policies",
         "aws_s3_buckets",
         "aws_iam_users",
         "aws_iam_user_attached_policies",
         "aws_s3_bucket_loggings",
         "aws_iam_user_access_keys",
         "aws_cloudtrail_trail_event_selectors",
         "aws_ec2_security_groups",
         "aws_iam_user_policies",
         "aws_cloudwatchlogs_metric_filters",
         "aws_cloudtrail_trails",
         "aws_cloudwatch_alarms",
         "aws_ec2_flow_logs",
         "aws_iam_virtual_mfa_devices",
         "aws_ec2_vpcs",
         "aws_iam_credential_reports",
         "aws_kms_keys",
         "aws_kms_key_rotation_statuses",
         "aws_sns_subscriptions"]
 destinations: ["postgresql"] # The destination for the data, in this case, PostgreSQL.
 skip_dependent_tables: true
 spec:
---
kind: destination
spec:
 name: "postgresql" # The type of destination, in this case, PostgreSQL.
 path: "cloudquery/postgresql" # The plugin path for handling PostgreSQL as a destination.
 registry: "cloudquery" # The registry from which the PostgreSQL plugin is sourced.
 version: "v8.5.2" # The version of the PostgreSQL plugin.

 spec:
   connection_string: "${POSTGRESQL_CONNECTION_STRING}"  # set the environment variable in a format like 
   # postgresql://postgres:pass@localhost:5432/postgres?sslmode=disable
   # You can also specify the connection string in DSN format, which allows for special characters in the password:
   # connection_string: "user=postgres password=pass+0-[word host=localhost port=5432 dbname=postgres"

Run the following command to sync the configuration: cloudquery sync config.yml

For more detailed instructions, you can check the page: CloudQuery AWS Plugin Documentation.

Visit CloudQuery Hub to browse individual plugins and access their detailed documentation.

Running Your dbt Project #

Navigate to your dbt project directory, where your dbt_project.yml resides. Make sure to have an existing profile in your profiles.yml that contains your PostgreSQL/Snowflake/BigQuery connection and authentication information.

If everything compiles without errors, you can then execute:

dbt run

This command will run all your dbt models and create tables/views in your destination database as defined in your models.

Note: If running locally, ensure you are using dbt-core and not dbt-cloud-cli as dbt-core does not require extra authentication.

Running Specific Models in dbt #

To execute a specific model along with its dependencies in your dbt project, use the --select option with the dbt run command. This command ensures that all dependencies for the specified model are also executed.

dbt run --select +<model_name>

Models #

The following models are available for PostgreSQL, Snowflake and Google BigQuery.

aws_compliance__cis_v1_2_0: AWS CIS V1.2.0.
aws_compliance__cis_v2_0_0: AWS CIS V2.0.0.
aws_compliance__cis_v3_0_0: AWS CIS V3.0.0.
aws_compliance__foundational_security: AWS Foundational Security.
aws_compliance__pci_dss_v3_2_1: AWS PCI DSS V3.2.1.
aws_compliance__imds_v2: IMDSv2 compliance checks.
aws_compliance__public_egress: Checks to find resources that can reach the public internet.
aws_compliance__publicly_available: Checks to find publicly accessible resources.

All of the models contain the following columns:

framework: The benchmark the check belongs to.
check_id: The check identifier (either a number or the service name and numberr).
title: The name/title of the check.
account_id: The AWS account id.
resource_id: The resource id (ARN).
status: The status of the check (fail / pass).

There are