dragoneye 0.0.77

Multi-cloud data scan tool

dragoneye

dragoneye is a Python tool that is used to collect data about a cloud environment using the cloud provider's APIs. It is intended to function as component in other tools who have the need to collect data quickly (multi-threaded), or as a command line to collect a snapshot of a cloud account.

dragoneye currently supports AWS (AssumeRole and AccessKey based collection) and Azure (with client secret).

Setup

Clone this git repository, navigate to the root directory where setup.py is located and run:

pip install .

(note the period at the end of the command)

We recommend doing this within a virtual environment, like so:

python3.9 -m venv ./venv
. ./venv/bin/activate
pip install .

Usage

Programmatic Usage

Create an instance of one of the CollectRequest classes, such as AwsAccessKeyCollectRequest, AwsAssumeRoleCollectRequest, AzureCollectRequest and call the collect function. For example:

from dragoneye import AwsScanner, AwsCloudScanSettings, AwsSessionFactory, AzureScanner, AzureCloudScanSettings, AzureAuthorizer, GcpCloudScanSettings, GcpCredentialsFactory, GcpScanner

### AWS ###
aws_settings = AwsCloudScanSettings(
    commands_path='/Users/dev/python/dragoneye/aws_commands_example.yaml',
    account_name='default', default_region='us-east-1', regions_filter=['us-east-1']
)

#### Using environment variables
session = AwsSessionFactory.get_session(profile_name=None, region='us-east-1')  # Raises exception if authentication is unsuccessful
aws_scan_output_directory = AwsScanner(session, aws_settings).scan()

#### Using an AWS Profile
session = AwsSessionFactory.get_session(profile_name='MyProfile', region='us-east-1')  # Raises exception if authentication is unsuccessful
aws_scan_output_directory = AwsScanner(session, aws_settings).scan()

#### Assume Role
session = AwsSessionFactory.get_session_using_assume_role(external_id='...',
                                                          role_arn="...",
                                                          region='us-east-1')
aws_scan_output_directory = AwsScanner(session, aws_settings).scan()

### Azure ###
azure_settings = AzureCloudScanSettings(
    commands_path='/Users/dev/python/dragoneye/azure_commands_example.yaml',
    subscription_id='...',
    account_name='my-account'
)

#### Using a registered application in Azure AD
token = AzureAuthorizer.get_authorization_token(
    tenant_id='...',
    client_id='...',
    client_secret='...'
)  # Raises exception if authentication is unsuccessful
azure_scan_output_directory = AzureScanner(token, azure_settings).scan()

### GCP ###
gcp_settings = GcpCloudScanSettings(commands_path='/Users/dev/python/dragoneye/gcp_commands_example.yaml',
                                    account_name='gcp', project_id='project-id')

# Authenticating by GCP default auth mechanism:
#    Checks environment in order of precedence:
#    - Environment variable GOOGLE_APPLICATION_CREDENTIALS pointing to
#      a file with stored credentials information.
#    - Stored "well known" file associated with `gcloud` command line tool.
#    - Google App Engine (production and testing)
#    - Google Compute Engine production environment.
default_credentials = GcpCredentialsFactory.get_default_credentials()
# Using a file that contains the service account credentials 
service_account_file_credentials = GcpCredentialsFactory.from_service_account_file('filepath.json')
# Using a dictionary that contains the service account credentials (the content of the file from above example)
service_account_dict_credentials = GcpCredentialsFactory.from_service_account_info({'...': '...'})
# Using impersonation method (service_account_A allowing service_account_B to generate short-lived credentials of service_account_A)
impersonation_credentials = GcpCredentialsFactory.impersonate(default_credentials, '[e-mail]', ['https://www.googleapis.com/auth/compute.readonly'])
# Authenticating from an AWS resource via a credentials config file defined by the 'Workload Identity Federation'
wif_credentials = GcpCredentialsFactory.from_aws_credentials_config_file('filepath.json')
# Same as above, but with the content of the above file
wif_credentials = GcpCredentialsFactory.from_aws_credentials_config_info({'...': '...'})

gcp_scan_output_directory = GcpScanner(default_credentials, gcp_settings)

CLI usage

For collecting data from AWS

Dragoneye will use the same mechanisms boto3 uses for authentication. It will generally look for AWS_ACCESS_KEY_ID, etc. as environment variables.

dragoneye aws

For collecting data from Azure

You can authenticate in one of two ways:

1. az login, which will allow dragoneye to use credentials loaded through Azure CLI.
2. With client id and secret of an application registered in your Azure AD.

dragoneye azure

For collecting data from GP

You can authenticate in several ways:

1. gcloud auth application-default login, which will allow dragoneye to use credentials loaded through GCP CLI.
2. With service account credentials - either a file, or its content.
3. With impersonation mechanism; service_account_A allowing service_account_B to generate short-lived credentials of service_account_A
4. With Workload Identity Federation mechanism, authenticating from AWS.

dragoneye gcp