Digital Forensics and Incident Response (DFIR) is at the forefront of cybersecurity. DFIR is where the edge of human and technological defensive capability meets the edge of human and technological offensive operations.

This continuous cat and mouse game has been ongoing for decades. It’s shaped by curiosity, fueled by competition, or sometimes just for the lulz. Constantly dealing with an evolving adversary requires timely, accurate, and consistent reaction and response. The output from DFIR can often inform financial, litigation, or legal actions, where the results are analyzed, reviewed, and critiqued in depth.

What does this look like without DFIR-as-Code?

In many organizations DFIR feels more like art than science. Analysts scramble to collect logs, dig into artifacts, and document procedures under pressure, with no guarantees of repeatability or accuracy. 

Manually processing and combing through data can be a good first step. Sophisticated adversaries, however, will take advantage of slow response time and any lack of capabilities. Paving well-travelled paths within your DFIR program is essential to shortening the OODA loop and evolving faster than the threat actors targeting your organization.

Ad-hoc approaches cause problems:

• Inconsistent response across incidents
• Slower investigations due to manual data collection
• Risk of error in high-stakes environments
• Missed evidence from unprocessed artifacts
• Hard-to-audit processes for legal or regulatory review

When an incident hits, this can be catastrophic. Time matters and slow, manual, ad-hoc procedures result in lost evidence, prolonged breaches, and missed opportunities.
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Enter DFIR-as-Code: From Manual to Automated

Just as DevOps revolutionized infrastructure through “Infrastructure-as-Code,” DFIR-as-Code extends the gains brought by Detection-Engineering, and brings structure, automation, and repeatability to the forensics and response lifecycle.

Maturing a DFIR program requires building up libraries of use cases and response procedures, much like continuous monitoring programs, and mature detection-engineering programs. This allows advanced organizations to adopt a very similar approach to their investigation and response processes: DIFR-as-Code.

DIFR-as-Code delivers:

• Automation of repeatable forensic and response actions
• Consistency across incidents, analysts, and teams
• Auditable workflows for regulatory compliance
• Scalable processes that don’t crumble under pressure
• Knowledge transfer across teams via codified playbooks

With DFIR-as-Code, humans stop reinventing the wheel for each incident. They focus on solving new problems, not rerunning the same play manually.
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What does DFIR-as-Code look like?

After an Initial Stimulus kicks off the process, the workflow for DFIR-as-Code repeats through 3 steps

• Collection
• Triage
• Contextualization

These 3 steps repeatedly feed the Incident Response process.

DFIR-as-Code workflow

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Collection

Collection is the process of acquiring and processing the artifacts and their associated metadata to produce timeline entries. Applied Security Data Strategy: A Leader’s Guide discusses how collection programs and data ingestion form the foundation of a mature data strategy. 
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Collection expands upon continuous Data Ingestion including point in time acquisition and processing of artifacts, such as the Master File Table from a host, metadata cloud resources, or the contents of etcd from a container.

The data gathered in Collection is often processed by tools such as FTK imager, Autopsy, Plaso, and Log2Timeline among others.

By maturing the collection portion of your DFIR lifecycle, you can ingest additional data in the same analytics platform as the rest of your data sources, providing a much more holistic view of the incidents and events taking place within your environment. Below is an example of the Abstract platform’s Plaso Log2timeline dashboard for analyzing forensic image output.
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Triage

Triage takes the Collected artifacts and timelines and applies signaling mechanisms to them. The signaling mechanisms can take the form of deterministic detection rules such as Sigma, Yara, Suricata rules and programmatic processors such as RegRipper, or inference mechanisms such as XGBoost based classifiers or scoring systems. 

Although implementing this logic can be challenging, it significantly improves the signal-to-noise ratio by correlating multiple data sources to confirm or refute observed activity.

Storing common Triage logic for different artifacts speeds response by confirming malicious activity, highlighting the most effective artifacts, and providing results in a familiar format that is easy to consume by the analyst.

Contextualization

Contextualization enriches the artifact summaries from Collection and Triage and correlates separate data sources to provide and scope a 360-degree view of the evidence.

Enriching data with Context can: 

• Highlight known infrastructure and/or user IPs from MFA logins, allowing those to be filtered out of an investigation if appropriate
• Match known user handles with their user identity
• Understand if attack traffic is being sent to vulnerable hosts
• Understanding how prevalent an executable is within your environment

Context allows your team to make higher-confidence assertions about what has or hasn’t occurred.

Next Steps

This entry outlines the DIFR-as-Code concept. This framework can be implemented and adopted to jump-start or rapidly mature DFIR programs within existing organizations, providing a foundational basis upon which to build.

Highly regulated environments and organizations can create well-defined approval processes that govern how they collect data while maintaining compliance and oversight. This enables faster, more well governed Collection while ensuring compliance and governance. By confirming that the requested data is both necessary and permitted, teams can operate successfully in heavily regulated environments.

DIFR-as-Code empowers teams to automate manual processes, enabling the teams to scale human effort and execute more. It ensures that artifacts and evidence are processed to at least a base level of confidence, allowing humans to decide upon additional artifact gathering and processing as necessary. 

Join us in the next entry to dive into applying this concept.