Cloudmarker is a cloud monitoring tool and framework.
Table of Contents:
What is Cloudmarker?
Cloudmarker is a cloud monitoring tool and framework. It can be used as a ready-made tool that audits your Azure or GCP cloud environments as well as a framework that allows you to develop your own cloud monitoring software to audit your clouds.
As a monitoring tool, it performs the following actions:
- Retrieves data about each configured cloud using the cloud APIs.
- Saves or indexes the retrieved data into each configured storage system or indexing engine.
- Analyzes the data for potential issues and generates events that represent the detected issues.
- Saves the events to configured storage or indexing engines as well as sends the events as alerts to alerting destinations.
Each of the above four aspects of the tool can be configured via a configuration file.
For example, the tool can be configured to pull data from Azure and index its data in Elasticsearch while it also pulls data from GCP and indexes the GCP data in MongoDB. Similarly, it is possible to configure the tool to check for unencrypted disks in Azure, generate events for it, and send them as alerts by email while it checks for insecure firewall rules in both Azure and GCP, generate events for them, and save those events in MongoDB.
This degree of flexibility to configure audits for different clouds in different ways comes from the fact that Cloudmarker is designed as a combination of lightweight framework and a bunch of plugins that do the heavylifting for retrieving cloud data, storing the data, analyzing the data, generating events, and sending alerts. These four types of plugins are formally known as cloud plugins, store plugins, event plugins, and alert plugins, respectively.
As a result of this plugin-based architecture, Cloudmarker can also be used as a framework to develop your own plugins that extend its capabilities by adding support for new types of clouds or data sources, storage or indexing engines, event generation, and alerting destinations.
One might wonder why we need a new project like this when similar projects exist. When we began working on this project in 2017, we were aware of similar tools that supported AWS and GCP but none that supported Azure at that time. As a result, we wrote our own tool to support Azure. We later added support for GCP as well. What began as a tiny proof of concept gradually turned into a fair amount of code, so we thought, we might as well share this project online, so that others could use it and see if they find value in it.
So far, some of the highlights of this project are:
- It is simple. It is easy to understand how to use the four types of plugins (clouds, stores, events, and alerts) to perform an audit.
- It is excellent at creating an inventory of the cloud environment.
- The data inventory it creates is easy to query.
- It is good at detecting insecure firewall rules and unencrypted disks. New detection mechanisms are coming up.
We also realize that we can add a lot more functionality to this project to make it more powerful too. See the Wishlist section below to see new features we would like to see in this project. Our project is hosted on GitHub at https://github.com/cloudmarker/cloudmarker. Contributions and pull requests are welcome.
We hope that you would give this project a shot, see if it addresses your needs, and provide us some feedback by posting a comment in our feedback thread or by creating a new issue.
Since Cloudmarker is not just a tool but also a framework, a lot of its functionality can be extended by writing plugins. However, Cloudmarker also comes bundled with a default set of plugins that can be used as is without writing a single line of code. Here is a brief overview of the features that come bundled with Cloudmarker:
- Perform scheduled or ad hoc audits of cloud environment.
- Retrieve data from Azure and GCP.
- Store or index retrieved data in Elasticsearch, MongoDB, Splunk, and the file system.
- Look for insecure firewall rules and generate firewall rule events.
- Look for unencrypted disks (Azure only) and generate events.
- Send alerts for events via email and Slack as well as save alerts in one of the supported storage or indexing engines (see the third point above).
- Normalize firewall rules from Azure and GCP which are in different
formats to a common object model (
"com") so that a single query or event rule can search for or detect issues in firewall rules from both clouds.
- Add more event plugins to detect different types of insecure configuration.
- Normalize other types of data into a common object model (
"com") just like we do right now for firewall rules.
Perform the following steps to set up Cloudmarker.
Create a virtual Python environment and install Cloudmarker in it:
python3 -m venv venv . venv/bin/activate pip3 install cloudmarker
Run sanity test:
The above command runs a mock audit with mock plugins that generate some mock data. The mock data generated can be found at
/tmp/cloudmarker/. Logs from the tool are written to the standard output as well as to
--nowoption tells Cloudmarker to run right now instead of waiting for a scheduled run.
To learn how to configure and use Cloudmarker with Azure or GCP clouds, see Cloudmarker Tutorial.
This section describes how to set up a development environment for Cloudmarker. This section is useful for those who would like to contribute to Cloudmarker or run Cloudmarker directly from its source.
We use primarily three tools to perform development on this project: Python 3, Git, and Make. Your system may already have these tools. But if not, here are some brief instructions on how they can be installed.
On macOS, if you have Homebrew installed, then these tools can be be installed easily with the following command:
brew install python git
On a Debian GNU/Linux system or in another Debian-based Linux distribution, they can be installed with the following commands:
apt-get update apt-get install python3 python3-venv git make
On a CentOS Linux distribution, they can be installed with these commands:
yum install centos-release-scl yum install git make rh-python36 scl enable rh-python36 bash
scl enablecommand starts a new shell for you to use Python 3.
On any other system, we hope you can figure out how to install these tools yourself.
Clone the project repository and enter its top-level directory:
git clone https://github.com/cloudmarker/cloudmarker.git cd cloudmarker
Create a virtual Python environment for development purpose:
make venv deps
This creates a virtual Python environment at
~/.venv/cloudmarker. Additionally, it also creates a convenience script named
venvin the current directory to easily activate the virtual Python environment which we will soon see in the next point.
To undo this step at anytime in future, i.e., delete the virtual Python environment directory, either enter
rm -rf venv ~/.venv/cloudmarkeror enter
Activate the virtual Python environment:
In the top-level directory of the project, enter this command:
python3 -m cloudmarker -n
This generates mock data at
/tmp/cloudmarker. This step serves as a sanity check that ensures that the development environment is correctly set up and that the Cloudmarker audit framework is running properly.
Now that the project is set up correctly, you can create a
cloudmarker.yamlto configure Cloudmarker to scan/audit your cloud or you can perform more development on the Cloudmarker source code. See Cloudmarker Tutorial for more details.
If you have set up a development environment to perform more development on Cloudmarker, please consider sending a pull request to us if you think your development work would be useful to the community.
Before sending a pull request, please run the unit tests, code coverage, linters, and document generator to ensure that no existing test has been broken and the pull request adheres to our coding conventions:
make test make coverage make lint make docs
To run these four targets in one shot, enter this "shortcut" target:
htmlcov/index.htmlwith a web browser to view the code coverage report.
docs/_build/html/index.htmlwith a web browser to view the generated documentation.
Here is a list of useful links about this project:
- Documentation on Read The Docs
- Latest release on PyPI
- Source code on GitHub
- Issue tracker on GitHub
- Changelog on GitHub
- Cloudmarker channel on Slack
- Invitation to Cloudmarker channel on Slack
To report bugs, suggest improvements, or ask questions, please create a new issue at http://github.com/cloudmarker/cloudmarker/issues.
This is free software. You are permitted to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of it, under the terms of the MIT License. See LICENSE.rst for the complete license.
This software is provided WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.rst for the complete disclaimer.