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Run a Datadog secrets scanning job in your GitHub Action workflows. This action wraps the Datadog Static Analyzer (that scans for secrets), invokes it against your codebase, and uploads the results to Datadog.
Create a file in .github/workflows
to run a Datadog secrets scanning job.
The following is a sample workflow file.
on: [push]
jobs:
check-quality:
runs-on: ubuntu-latest
name: Datadog Static Analyzer
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Check code meets quality standards
id: datadog-static-analysis
uses: DataDog/datadog-static-analyzer-github-action@v1
with:
dd_app_key: ${{ secrets.DD_APP_KEY }}
dd_api_key: ${{ secrets.DD_API_KEY }}
dd_site: "datadoghq.com"
cpu_count: 2
enable_performance_statistics: false
static_analysis_enabled: false
secrets_enabled: true
You must set your Datadog API and application keys as secrets in your GitHub repository, at either the organization or repository level. Ensure that you add the code_analysis_read
scope to your Datadog application key. For more information, see API and Application Keys.
Make sure to replace dd_site
with the Datadog site you are using.
You can set the following parameters for Static Code Analysis.
Name | Description | Required | Default |
---|---|---|---|
dd_api_key | Your Datadog API key. This key is created by your Datadog organization and should be stored as a secret. | Yes | |
dd_app_key | Your Datadog application key. This key is created by your Datadog organization and should be stored as a secret. | Yes | |
dd_site | The Datadog site to send information to. | No | datadoghq.com |
cpu_count | Set the number of CPUs used to by the analyzer. | No | 2 |
enable_performance_statistics | Get the execution time statistics for analyzed files. | No | false |
debug | Lets the analyzer print additional logs useful for debugging. To enable, set to yes . | No | no |
architecture | The CPU architecture to use for the analyzer. Supported values are x86_64 and aarch64 . | No | x86_64 |