TFCB Drift Detection Report

This project will search a TFCB organization and report on workspaces where drift is enabled or disabled. If the workspace is configured for drift it will report if drift has been detected and when the last run occured.

It will output a csv report with the workspace name, if drift is configured on the workspace, has drift been detected, and the last time drift was checked.

An example of the output:

workspace_name,drift_configured,drift_detected,drift_last_checked
azure_basic_synk_infracost,True,False,
test,False,,
api-test,False,,
azure_basic_bridgecrew,False,,
cli_workflow,False,,
github_action_demo,False,,
tf_packer_demo,False,,
hashicat-azure,False,,
terraform_esxi,False,,
tf_module_test,False,,
generate_aws_creds,False,,

Requirements

Developed on python==3.10.4

Python dependancies documented in requirements.txt

Variables

This project uses python-dotenv library. This expects a file called .env which contains the environment variables. An example .env has been included in .env.example.

Variable	Type	Description	Example
TFCB_URL	string	Protocol and domain for API calls	https://app.terraform.io
TFCB_ORG	string	Name of the TFCB Organization	example_org
TFCB_API_KEY	string	API key created in TFCB	a1b2c3d4
OUTPUT_DIR	string	name of directory to write the output file	output

Local Execution

Virtual environments are highly recommended for local execution. A Pipfile is included if you are using pipenv. If you are not using pipenv you can create a virtual environment using the default venv.

If you are creating a virtual environment using venv you will need to install the dependancies: pip install -r requirements.txt

After creating the .env file and virtual environment you can run the script by typing in python main.py.

Docker Execution

A Docker file is included to simplify Python and its dependencies.

To build the image: docker build -t drift_container .

To run the image: docker run -v $(pwd):/home/devuser -ti drift_container

The -v will mount the local directory to the /home/devuser directory. This will allow the container to read and write from the local directory where the script resides.

After launching the docker container type in python main.py to run the script.