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https://github.com/complianceascode/auditree-arboretum

The Auditree common fetchers, checks and harvest reports library.
https://github.com/complianceascode/auditree-arboretum

compliance compliance-as-code compliance-automation continuous-compliance devops devsecops python

Last synced: 19 days ago
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The Auditree common fetchers, checks and harvest reports library.

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README 

        
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# auditree-arboretum



The Auditree common fetchers, checks and [harvest][] reports library.



## Introduction



Auditree Arboretum is a Python library of common compliance fetchers, checks & harvest

reports built upon the [Auditree compliance automation framework][auditree-framework].



## Repo content



### Functionality categorization



Arboretum fetchers, checks, and Harvest reports are organized into functional

grouping categories.  The following categories have either been contributed to

or will be contributed to in the near future.  We anticipate that this list will

grow as arboretum matures.



- [Ansible](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/ansible)

- [Auditree](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/auditree)

- [Chef](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/chef)

- [IBM Cloud](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/ibm_cloud)

- [Kubernetes](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/kubernetes)

- [Object Storage](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/object_storage)

- [Pager Duty](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/pager_duty)

- [Splunk](https://github.com/ComplianceAsCode/auditree-arboretum/tree/main/arboretum/splunk)



### Fetchers



Please read the framework documentation for [fetcher design principles][] before

contributing a fetcher.



Fetchers must apply no logic to the data they retrieve. They must write unadulterated

(modulo sorting & de-duplication) into the `/raw` area of the locker via the

framework-provided decorators or context managers.



Fetchers must be atomic - retrieving and creating the data they are responsible

for. Fetcher execution order is not guaranteed and so you must not assume that

evidence already exists and is current in the locker.  Use

[evidence dependency chaining][] if a fetcher depends on evidence gathered by another

fetcher in order to gather its intended evidence.



Fetchers should be as fast as the API call allows. If a call is long running it

should be separated into a dedicated evidence providing tool, which places data

where a fetcher can retrieve it easily & quickly.



### Checks



Please read the framework documentation for [check design principles][] before

contributing a check.



Checks should only use evidence from the evidence locker to perform check operations.

Also, checks **should not** write or change evidence from the evidence locker.  That

is the job of a fetcher.



[Jinja][] is used to produce reports from checks.  As such each check class must have

at least one associated report template in order to produce a check report.  In keeping

with the "DevSecOps" theme, check reports are meant to provide details on violations

identified by checks.  These violations are in the form of failures and warnings.

**They aren't meant to be used to format fetched raw evidence into a readable report.**

[Harvest][harvest] reports should be used to satisfy that need.



### Harvest Reports



Harvest reports are hosted with the fetchers/checks that collect the evidence for

the reports process. Within `auditree-arboretum` this means the harvest report code

lives in `reports` folders throughout this repository. For more details check out

[harvest report development][harvest-rpt-dev] in the [harvest][harvest] README.



## Usage



`arboretum` is available for download from [PyPI](https://pypi.org/project/auditree-arboretum/).



### Prerequisites



- Supported for execution on OSX and LINUX.

- Supported for execution with Python 3.6 and above.



### Integration



Follow these steps to integrate auditree-arboretum fetchers and checks into your project:



* Add this `auditree-arboretum` package as a dependency in your Python project.

* The following steps can be taken to import individual arboretum fetchers and checks.

  * For a fetcher, add a `fetch__common.py` module, if one does not already

  exist, in your project's `fetchers` path where the `` is

  the respective category folder within this repo of that fetcher.  Having a separate

  common "category" module guards against name collisions across categories.

  * For a check, add a `test__common.py` module, if one does not already exist,

  in your project's `checks` path where the `` is the respective category folder

  within this repo of that check.  Having a separate common "category" module guards

  against name collisions across providers and technologies.

  * Import the desired fetcher or check class and the `auditree-framework` will handle

  the rest.



  For example to use the Abandoned Evidence fetcher from the `auditree` category, add

  the following to your `fetch_auditree_common.py`:



  ```python

  from arboretum.auditree.fetchers.fetch_abandoned_evidence import AbandonedEvidenceFetcher

  ```



* `auditree-arboretum` fetchers and checks are designed to execute as part of a downstream

Python project, so you may need to setup your project's configuration in order for the

fetchers and checks to execute as desired.  Each category folder in this repository

includes a README.md that documents each fetcher's and check's configuration.

  * In general `auditree-arboretum` fetchers and checks expect an `org` field with content

  that captures each fetcher's and check's configuration settings.



  For example:



  ```json

  {

    "org": {

      "auditree": {

        "abandoned_evidence": {

          "threshold": 1234567,

          "exceptions": {

          "raw/path/to-evidence.json": "This is a good reason",

          "raw/path/to-evidence-2.json": "This is also a good reason"

        }

      }

    }

  }

  ```



* Finally, for a check, be sure to add the appropriate entry into your project's

``controls.json`` file.  Doing this allows you to group checks together as a control

set which is useful for organizing check notifications and targeted check execution.



  For example to use the Abandoned Evidence check, add something similar to the

  following to your project's `controls.json`:



  ```json

  {

    "arboretum.auditree.checks.test_abandoned_evidence.AbandonedEvidenceCheck": {

      "auditree_evidence": {

        "auditree_control": ["arboretum.auditree"]

      }

    }

  }

  ```



[platform-badge]: https://img.shields.io/badge/platform-osx%20|%20linux-orange.svg

[pre-commit-badge]: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white

[python-badge]: https://img.shields.io/badge/python-v3.6+-blue.svg

[pre-commit]: https://github.com/pre-commit/pre-commit

[python-dl]: https://www.python.org/downloads/

[lint-test]: https://github.com/ComplianceAsCode/auditree-arboretum/actions?query=workflow%3A%22format+%7C+lint+%7C+test%22

[pypi-upload]: https://github.com/ComplianceAsCode/auditree-arboretum/actions?query=workflow%3A%22PyPI+upload%22

[auditree-framework]: https://github.com/ComplianceAsCode/auditree-framework

[harvest]: https://github.com/ComplianceAsCode/auditree-harvest

[fetcher design principles]: https://complianceascode.github.io/auditree-framework/design-principles.html#compliance-fetchers

[evidence dependency chaining]: https://complianceascode.github.io/auditree-framework/design-principles.html#evidence-dependency-chaining

[check design principles]: https://complianceascode.github.io/auditree-framework/design-principles.html#compliance-checks

[Jinja]: https://palletsprojects.com/p/jinja/

[harvest-rpt-dev]: https://github.com/ComplianceAsCode/auditree-harvest#report-development