For those of us working in digital forensics and incident response (DFIR), keeping up with the cloud revolution can feel overwhelming at times. We're experts in tracking down security incidents and understanding what went wrong, but many of us aren't DevOps engineers by trade. That’s okay—it’s not necessary to become a full-time cloud architect to take advantage of the powerful automation tools and workflows available in platforms like AWS. Instead, we can collaborate with engineers and developers who specialize in these areas to create effective, scalable solutions that align with our needs.
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Getting Started with Cloud-Based Forensics
For those who are new to the cloud or want a quick start to cloud forensics, Amazon Machine Images (AMIs) are a great option. AMIs are pre-configured templates that contain the information required to launch an instance. If you’re not yet ready to build your own custom AMI, there are existing ones you can use.
SIFT (SANS Investigative Forensic Toolkit) is a popular option for forensics analysis and is available as an AMI. While it’s not listed on the official AWS Marketplace, you can find the latest AMI IDs on the github page and launch them from the EC2 console.
Security Onion is another robust tool for network monitoring and intrusion detection. They publish their releases as AMIs, although there’s a small charge to cover regular update services. If you want full control, you can build your own AMI from their free distribution.
As your team grows in its cloud forensics capabilities, you may want to create custom AMIs to fit specific use cases. EC2 Image Builder is a helpful AWS service that makes it easy to create and update AMIs, complete with patches and any necessary updates. This ensures that you always have a reliable, up-to-date image for your incident response efforts.
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Infrastructure-as-Code: A Scalable Approach to Forensics Environments
As your organization expands its cloud infrastructure, it's essential to deploy forensics environments quickly and consistently. This is where Infrastructure-as-Code (IaC) comes into play. IaC allows you to define and manage your cloud resources using code, making environments easily repeatable and reducing the risk of configuration drift.
One of the key principles of IaC is idempotence. This means that, no matter the current state of your environment, running the IaC script will bring everything to the desired state. This makes it easier to ensure that forensic environments are deployed consistently and accurately every time.
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CloudFormation and Terraform
AWS provides its own IaC tool called CloudFormation, which uses JSON or YAML files to define and automate resource configurations. AWS also offers CloudFormation templates for various use cases, including incident response workflows. These templates can be adapted to fit your specific needs, making it easy to set up response environments quickly. You can explore some ready-to-use templates.
However, if your organization operates across multiple cloud providers—such as Azure, Google Cloud, or DigitalOcean—you might prefer an agnostic solution like Terraform. Terraform, developed by HashiCorp, allows you to write a single set of scripts that can be applied to various cloud platforms, streamlining deployment across your entire infrastructure.
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Automating Forensic Tasks with AWS Lambda
One of the most exciting aspects of cloud-based forensics is the potential for automation, and AWS Lambda is a key player in this space. Lambda lets you run code without provisioning servers, and it’s event-driven, meaning it automatically executes tasks in response to certain triggers. This is perfect for incident response, where every second counts.
For example, let’s say you’ve set up a write-only S3 bucket for triage data. Lambda can be triggered whenever a new file is uploaded, automatically kicking off a series of actions such as running a triage analysis script or notifying your response team. The best part is that you’re only charged for the execution time, not for keeping a server running 24/7.
Lambda supports multiple programming languages, including Python, Node.js, Java, Go, Ruby, C#, and PowerShell. This flexibility makes it easy to integrate with existing workflows, no matter what scripting languages you’re comfortable with.
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AWS Step Functions: Orchestrating Complex Workflows
While Lambda excels at executing individual tasks, AWS Step Functions allow you to orchestrate complex, multi-step workflows. In the context of incident response, this means you can automate an entire forensics investigation, from capturing an EC2 snapshot to running analysis scripts and generating reports.
One example of a Step Function workflow comes from the AWS Labs project titled “EC2 Auto Clean Room Forensics”. Here’s how the workflow operates:
Capture a snapshot of the target EC2 instance’s volumes.
Notify the team via Slack that the snapshot is complete.
Isolate the compromised EC2 instance.
Create a pristine analysis instance and mount the snapshot.
Use the AWS Systems Manager (SSM) agent to run forensic scripts on the instance.
Generate a detailed report.
Notify the team when the investigation is complete.
This kind of automation significantly speeds up the forensic process, allowing your team to focus on higher-level analysis rather than repetitive tasks.
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Other Automation Options for Forensics in the Cloud
If you don’t have the resources or time to dive deep into AWS-specific solutions, there are plenty of other automation options available that work across cloud platforms.
For instance, dfTimewolf, developed by Google’s IR team, is a Python-based framework designed for automating DFIR workflows. It includes recipes for AWS, Google Cloud Platform (GCP), and Azure, allowing you to streamline evidence staging and processing across multiple cloud environments.
Alternatively, if you’re comfortable with shell scripting and the AWS CLI, you can develop your own lightweight automation scripts. For example, Recon InfoSec has released a simple yet powerful project that ingests triage data from S3 and processes it in Timesketch. This is an excellent way to automate data handling without building a complex pipeline from scratch.
https://dftimewolf.readthedocs.io/en/latest/developers-guide.html
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The Importance of Practice in Cloud Incident Response
Automation can dramatically improve your response times and overall efficiency, but it’s essential to practice these workflows regularly. Cloud technology evolves rapidly, and so do the risks associated with it. By practicing response scenarios—whether using AWS Step Functions, Terraform, or even simple CLI scripts—you can identify gaps in your processes and make improvements before a real incident occurs.
AWS also provides several incident response simulations that allow you to practice responding to real-world scenarios. These are excellent resources to test your workflows and ensure that your team is always ready.
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Conclusion
Stay proactive by experimenting with these technologies, practicing regularly, and continuously refining your workflows. Cloud adoption is accelerating, and with it comes the need for robust, automated incident response strategies that can keep up with this evolving landscape
Akash Patel
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