By Hans Henseler, Professor of Digital Forensics & E-Discovery, University of Leiden Applied Sciences, and Senior Digital Forensic Scientist at the Netherlands Forensic Institute.
Introduction
In hindsight, 2021 was a significant inflection point in the world of artificial intelligence, characterized by remarkable developments in deep learning, manifesting in models such as DALL·E, CLIP, and in models that were surpassing GPT-3 in size and ability. These advancements hinted at a future not limited to machines performing computational tasks, but also emulating intricate human-like activities. However, it was in November 2022, with the emergence of ChatGPT, that the world glimpsed a truly transformative tool, suggesting potential applications even in niches like digital forensics [1,2].
Yet, the digital forensics community, by and large, has yet to fully embrace or debate the profound implications of these advancements. Every case in digital forensics presents its own universe of data, sourced from confiscated devices, cloud accounts, and other digital touchpoints. Parsing through this enormity, especially with looming backlogs in forensic labs and aspirations to involve detectives without specialized forensic training, demands a radical rethinking of our tools and approaches.
Instead of merely focusing on the limitations or potential pitfalls of Large Language Models (LLMs), we ought to explore their promise. Retrieval-Augmented Generation (RAG) is one such promising frontier. By coupling real-time data retrieval with the robust capabilities of generative models, RAG offers a compelling case for the next evolutionary step in digital forensics. This article emphasizes not just the challenges, but also the transformative potential of....
