This blog post will demonstrate how to create a custom Python search command for Splunk and will demystify common roadblocks such as: how to create a custom search command with Python, how to store secrets for a custom search command, and how to install external Python libraries. With each roadblock discussed we will also cover the solution as code examples and hands-on exercises. To do this, we must first start with an introduction to the architecture of a custom Python search command.
The industry has gravitated towards using Jupyter notebooks for automating incident response and threat hunting. However, one of the biggest barriers for any application/automation is the ability to store secrets (username+passwords, API keys, etc) to access other services. This blog post will demonstrate how to use Vault to store secrets and integrate the ability to retrieve secrets from Vault with Jupyter Notebooks to assist in automating your security operations.
A common question in the #Kolide channel in the Osquery Slack is how to use the Kolide Fleet API. Kolide Fleet is written in GoLang and utilizes the GoKit framework to build the application. Therefore, almost every action that can be performed via the WebGUI is an API call. This blog post is going to demonstrate how to use the Kolide Fleet API to perform actions such as creating a live query and obtaining the results using Python websockets, obtaining the Osquery enroll secret, and creating a saved query. In addition to the API, this blog post will demonstrate how to use the Fleetctl command-line tool to perform the same actions. Lastly, this blog post includes an Ansible playbook to automate deploying Oquery agents and registering them with Kolide.
Facebook released an awesome open-source tool named Osquery that is being maintained by a thriving community supported by the Linux Foundation and several product leaders such as Kolide, TrailOfBits, and Uptycs. However, Facebook did not release the server component of Osquery and that has led to the creation of many projects: Kolide, Uptycs, Doorman, OSCRTL, and SGT just to name a few. Furthermore, not all projects have the ability to support the Osquery file carve functionality, more specifically the open-source version of Kolide Fleet. This project set out on a mission to provide an open-source Osquery file carving server for file uploads and downloads that could be used with Kolide.
This blog will provide a deep dive into the architecture of this project, design decisions, and lessons learned as an evolving incident response engineer. This project has been a 6-month long effort that resulted in the creation of 4 blog posts, 3 Udemy certificates/courses, and 3 separate Github repos. The collection of these experiences and research has led to the creation of this project. My hope is that this project benefits the community and provides an additional capability to Osquery that may not be supported by all fleet managers.
In incident response, there is a disconnect between a security alert being generated and a user’s confirmation of the security alert. For example, generating an alert every time a user runs “curl” on a production system would generate a bunch of false positives that can lead to what is called “alert fatigue”. But if we extend our incident response capabilities to include the user as part of the triage process we could reduce the number of alerts. This blog post is going to demonstrate AskJeevesSecBot which is an open-source proof of concept (POC) of how to integrate Slack and user responses into your security pipeline, specifically during the triage phase of the incident response process. In addition to a PoC, this blog post will also provide a deep dive into the architecture of this project, design decisions, and lessons learned as an evolving threat detection engineer.
For several years I have always wanted to write an Osquery extension to perform memory dumps and analysis. I never got the time to do a deep dive into my idea but since I have been creating some Osquery-go extensions lately, I decided to take a crack at my idea. This blog post will provide a high overview of the architecture of these Osquery extensions for this project, how to generate memory dumps, and how to remotely analyze these memory dumps with Osquery. Follow me with another threat detection engineering experience with Osquery-go.
This blog post is going to cover an Osquery extension that I engineered with osquery-go to eradicate malware. This extension has the ability to delete files, kill processes, delete directories, and can be used with the builtin YARA table. This blog post will act as documentation for the setup and operation of this Osquery extension.
Here we go again! This blog post is tangential to my previous blog post on creating an Osquery extension with Python but this time we are using golang. Osquery is my favorite open-source security tool and golang is becoming a popular programming language so fusing them together allows us to engineer tools to detect threats. This blog post will build an Osquery-go extension to calculate the CommunityID of a network connection utilizing the Osquery-polylogyx extension pack to monitor network connections. In blog posts to follow, we will correlate network-based events monitored by Zeek and host-based events generated by Osquery using the CommunityID. So follow me again as your adventure guide on this development journey to make an Osquery extension with osquery-go.
Osquery is my favorite open-source security tool and Python is my favorite programming language so fusing them together allows us to engineer tools to detect threats. This blog post will build an Osquery-python extension to calculate the CommunityID of a network connection utilizing the Osquery-polylogyx extension pack to monitor network connections. In blog posts to follow, we will correlate network-based events generated by Zeek and host-based events generated by Osquery using the CommunityID. So follow me as your adventure guide on this development journey to make an Osquery extension with osquery-python.