This is a 100% serverless tool that analyzes GCP Organization Policies for updates and then posts to a slack channel as well as twitter via our Twitter bot.
- A Cloud Scheduler job kicks off the comparison every hour and sends a base64 encoded message to Cloud Pub/Sub.
- Cloud Pub/Sub forwards the encoded message to a Cloud Function via a Pub/Sub Subscription.
- The Cloud Function receives the message, decodes it, and announces the beginning of the comparison.
- The Cloud Function searches for a pre-existing Organization Policy Constraint baseline file in a GCS bucket.
- If a baseline file exists: it copies the file locally for comparison.
- If a baseline file does not exist in the GCS bucket: The function creates a baseline based on the current available Organization Policy Constraints and then uploads to GCS for future comparisons.
- After the baseline is copied locally (if a baseline existed), the Cloud Function queries the available Organization Policy Constraints performs a comparison.
- If there are updates, the new Organization Policy Constraint list that was generated becomes the new baseline and is updated to the GCS bucket for future comparisons. The Cloud Function then moves on to steps 6, 7, and 8.
- The Cloud Function creates a GitHub Pull Request with the new Organization Policies which can be found here.
- The Cloud Function posts to Twitter via the handle @gcporgpolicybot and includes the new constraints plus a link to the GitHub commit.
- In addition to a Twitter post, the Cloud Function will post in a Slack Channel alerting the participants of the new constraints.
- Update or comment out the
backend.tffile for the terraform state file.
terraform {
backend "gcs" {
bucket = "<insert_value_here>"
prefix = "<insert_value_here>"
}
}
-
In order to keep secrets out of the Terraform state file, we recommend manually uploading the Slack Webhook into GCP Secrets Manager and then referencing that in the below terraform variables.
-
Fill in the required values for the
terraform.tfvarsfile. We recommend an isolated project for this solution as well as a separate GCS bucket for your policy file and the function's code filesrc.zip
project_id = ""
org_id = ""
secret_project = ""
name_prefix = ""
secret_slack_name = ""
secret_token_name = ""
secret_version = ""
twitter_consumer_key_name = ""
twitter_consumer_key_secret_name = ""
twitter_access_token_name = ""
twitter_access_token_secret_name = ""
policy_bucket_location. = ""
- Clone the repository locally:
git clone [email protected]:ScaleSec/gcp_org_policy_notifier.git
- Create your virtual environment:
python3 -m venv my_venv
- Activate environment and install dependencies:
source my_venv/bin/activate
pip install -r src/requirements.txt
- Deploy via terraform:
terraform init
terraform plan
terraform apply
| Name | Description | Type | Default | Required |
|---|---|---|---|---|
| bucket_force_destroy | When deleting the GCS bucket containing the cloud function, delete all objects in the bucket first. | bool |
true |
no |
| file_location | Location to store the org policy file in the Cloud Function. Needs to be in /tmp/. | string |
"/tmp/policies.txt" |
no |
| function_available_memory_mb | The amount of memory in megabytes allotted for the function to use. | number |
2048 |
no |
| function_description | The description of the function. | string |
"Compares Org Policies and alerts users." |
no |
| function_entry_point | The name of a method in the function source which will be invoked when the function is executed. | string |
"announce_kickoff" |
no |
| function_event_trigger_failure_policy_retry | A toggle to determine if the function should be retried on failure. | bool |
false |
no |
| function_perms | The Cloud Function custom IAM role permissions. Must be a list. | list |
[ |
no |
| function_runtime | The runtime in which the function will be executed. | string |
"python37" |
no |
| function_source_directory | The contents of this directory will be archived and used as the function source. | string |
"./src" |
no |
| function_timeout_s | The amount of time in seconds allotted for the execution of the function. | number |
60 |
no |
| job_description | The description of the Cloud Scheduler. | string |
"Starts Organization Policies check." |
no |
| job_schedule | The job frequency, in cron syntax. The default is every hour. | string |
"0 * * * *" |
no |
| message_data | The data to send in the topic message. | string |
"U3RhcnRpbmcgQ29tcGFyaXNvbg==" |
no |
| name_prefix | The prefixed used to name resources | string |
n/a | yes |
| org_id | The GCP Org ID to assign permissions to. | any |
n/a | yes |
| policy_file | The name of the Org policy file in the GCS bucket. | string |
"policies.txt" |
no |
| project_id | The ID of the project where the resources will be created. | string |
n/a | yes |
| region | The region in which resources will be applied. | string |
"us-central1" |
no |
| scheduler_job | An existing Cloud Scheduler job instance. | object({ name = string }) |
null |
no |
| secret_project | The GCP project the Slack Webhook is stored. | any |
n/a | yes |
| secret_slack_name | The name of the Slack Webhook secret in GCP. | any |
n/a | yes |
| secret_token_name | The name of the GitHub token secret in GCP. | any |
n/a | yes |
| secret_version | The version of the Slack Webhook secret in GCP. Leave as an empty string to use 'latest' | string |
"latest" |
no |
| time_zone | The timezone to use in scheduler. | string |
"America/Detroit" |
no |
| twitter_access_token_name | The name of the Twitter Access Token secret in GCP. | any |
n/a | yes |
| twitter_access_token_secret_name | The name of the Twitter Access Token Secret secret in GCP. | any |
n/a | yes |
| twitter_consumer_key_name | The name of the Twitter Consumer Key secret in GCP. | any |
n/a | yes |
| twitter_consumer_key_secret_name | The name of the Twitter Consumer Key Secret secret in GCP. | any |
n/a | yes |
Feedback is welcome and encouraged via a GitHub issue. Please open an issue for any bugs, feature requests, or general improvements you would like to see. Thank you in advance!