The Auditree common fetchers, checks and harvest reports library.
Auditree Arboretum is a Python library of common compliance fetchers, checks & harvest reports built upon the Auditree compliance automation framework.
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.
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.
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 reports should be used to satisfy that need.
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 in the harvest README.
arboretum
is available for download from PyPI.
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.
fetch_<category>_common.py
module, if one does not already
exist, in your project's fetchers
path where the <category>
is
the respective category folder within this repo of that fetcher. Having a separate
common "category" module guards against name collisions across categories.test_<category>_common.py
module, if one does not already exist,
in your project's checks
path where the <category>
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.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
:
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.
auditree-arboretum
fetchers and checks expect an org
field with content
that captures each fetcher's and check's configuration settings.For example:
{
"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
:
{
"arboretum.auditree.checks.test_abandoned_evidence.AbandonedEvidenceCheck": {
"auditree_evidence": {
"auditree_control": ["arboretum.auditree"]
}
}
}