Defining Automation in Digital Forensics

By Dr. Sampath LonkaReviewed by Susha Cheriyedath, M.Sc.Jun 28 2023

In a recent study published in the journal Forensic Science International, researchers explore the concept of automation in digital forensics and the challenges, considerations, and perspectives surrounding automation adoption.

Study: Automation for Digital Forensics: Towards a definition for the community. Image Credit: Zapp2Photo / Shutterstock.com

Background

Automation has been widely used in digital forensics, with tools developed to analyze file systems, recover deleted files, and perform keyword searches. Nevertheless, there are gaps and varying opinions on what tasks qualify as automation.

Time and data volume limitations drive the need for automation to reduce manual workload. The use of artificial intelligence (AI) has also increased in recent years; however, challenges persist in automating higher-level processes.

In the current study, the methodology involved researching definitions, consulting academics, and conducting practitioner interviews to refine the definition.

Defining automation

In driving automation levels, "assisted" refers to a system with supporting functionality where the driver is responsible for monitoring. Comparatively, "automated" is used to describe a system that temporarily takes over driving but requires the driver to respond within a reasonable time, whereas "autonomous" means the system is fully responsible.

Digital forensics uses "assisted" and "automated" interchangeably. Each investigation follows a method with a series of tasks that must be completed, with some tasks also having sub-tasks. Automation aims to improve efficiency, reliability, and transparency by reducing or eliminating human involvement.

Software or hardware that completes a task more efficiently, reliably, or transparently by reducing or removing the need for human engagement”

Automation is most effective in tasks with defined structures, such as translation, calculations, and data comparison. Advancements in AI enable exploration of decision-making automation.

Important considerations

Defining automation in digital forensics is crucial for understanding its implications and managing its associated risks. Full automation remains challenging; however, partial automation has proven valuable in this field.

Categorizing automation levels and developing suitable interfaces are ongoing challenges. Investigators' skills and resources influence adoption, while over-reliance on automation raises concerns about investigative quality and biases.

Automation is effective in structured tasks like translation, calculations, and data comparison, with AI advancements supporting the development of decision-making automated systems.

Automation in digital forensics

Automation in digital forensics ranges from basic automated tasks to fully autonomous investigations. Nevertheless, reports solely generated by automated systems are not universally accepted.

Differentiating between automation categories is challenging. As a result, a spectrum approach has been proposed until a categorization scheme is developed.

The lack of automation is attributed to interface limitations and data format interoperability issues. Overcoming these challenges requires retrofitting missing functionality, developing new tools, or implementing data exchange facilitation.

Investigators' knowledge, motivation, and resources play a significant role in automation adoption. Limited skills, reluctance to develop new tools and resource constraints hinder progress. The unique nature of each investigation poses challenges in developing universally applicable automation solutions.

Challenges

Some of the challenges in automation include diminished investigative skills, potential quality reductions, and over-reliance on tools. Bugs and biased algorithms can lead to incorrect results, while lack of transparency complicates error detection and validation. Risks associated with automation, such as blackboxes and explainable AI, must be further studied.

Automation from a practitioner’s point of view

Three interviews were conducted with law enforcement professionals to explore their perspectives on automation. Interviewees defined automation as using machines to replace repetitive tasks, save time, and address disliked or complex activities.

Administrative tasks were identified as the most common repetitive tasks encountered in automation. Automation was considered feasible for these tasks; however, they were often overlooked or automated only for accounting purposes.

Tasks requiring expert judgment were considered to be difficult to automate due to the lack of standardized procedures. Stable inputs, profitability, and understanding the underlying processes were highlighted as requirements for automation.

These interviews also revealed challenges related to the evolving nature of applications, sharing knowledge, and assessing the reliability of tools.

Current trends in enabling automation

Digital Forensics as a Service centralizes data processing, while Robotic Process Automation automates back-office tasks.

Workflow automation optimizes the time and hardware usage. Cyber-investigation Analysis Standard Expression provides standardized information representation.

These trends aim to streamline processes, enhance efficiency, and promote collaboration in digital forensics. However, a unified definition and standardized framework for automation in the field remains needed.

Conclusions

Automation in digital forensics presents unique challenges, with varying definitions and perspectives emphasizing efficiency and reducing human effort. AI has advanced automation; however, a unified definition and standardized framework are lacking. Transparency and evidence integrity are crucial to successfully integrating automation into scientific fields like digital forensics. There remains an urgent need for community input, addressing terminology, differentiation, and feasibility.