Jupyter Notebook is often thought of as an application for data science and machine learning. However Jupyter Notebook is a web-based application to run and document code for any use case. This feature makes Jupyter Notebook an excellent host for running incident response playbooks. Incident response involves gathering and interpreting data, which is the strength of the Jupyter Notebook application.
The Jupyter notebooks in this repo will help security analysts and responders query logs (such as CloudTrail, DNS logs, Security Lake logs, etc.) and automate the response to incidents using AWS API calls (such as automating the deactivation of access keys) and non-AWS API calls. Additionally, you can combine automation in the form of python code cells and documentation with Markdown.
This repo includes several artifacts such as CloudFormation template and incident response playbooks in the form of Jupyter notebooks. We will walk you through how you can create an environment on AWS to interact with the Jupyter notebooks, how to use the Jupyter notebooks, and how you can build your own!
There are several ways to interact with Jupyter notebooks. For this solution, you will host your Jupyter notebooks on a SageMaker notebook instance. We chose to use SageMaker instead of running the notebooks locally because SageMaker provides flexible compute, seamless integration with CodeCommit and GitHub, temporary credentials through AWS Identity and Access Management (IAM) roles, and lower latency for Athena queries.
You can deploy the SageMaker notebook instance by using the AWS CloudFormation template from the cfn-templates folder.
The solution uses Athena to query CloudTrail logs in this scenario. You need to create an Athena table for CloudTrail.
There are two main ways of creating an Athena table for CloudTrail.
- First is through the AWS Security Analytics Bootstrap. We highly recommend using this bootstrap because you can perform security investigations on different types of AWS service logs. To get the CloudFormation template for the bootstrap, see Athena_infra_setup.yml. Here is additional guidance on deploying the bootstrap: Video guide.
- Alternatively, you can create your Athena table for CloudTrail logs through the CloudTrail console. For instructions, see Using the CloudTrail console to create an Athena table for CloudTrail logs. One advantage of this approach is that it will be quicker to set up.
There is a folder in this repo titled playbooks. It contains subfolders for different types of playbooks. Inside of each playbook folder, there are two notebooks: one for detection and one for containment.
We recommend that you clone this repo into the environment you use to work with Jupyter notebooks - either your local machine or your SageMaker Notebook Instance.
Inside of the playbooks folder, there are detailed instructions on how to use the notebooks provided.
Additionally, we highly encourage you to give us your feedback on this repo as well as suggestions on things that you want to see next.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
