Integrating the DeepSeek-R1 model as an analytical assistant in digital forensic investigations of corruption cases - Hafni Ferdian; Ruki Harwahyu (Komisi Pemberantasan Korupsi, 2025)
This article examines the integration of the DeepSeek-R1 large language model as an analytical assistant in digital forensic investigations, particularly in corruption cases. The growing volume of digital evidence often leads to substantial analysis backlogs that can extend for months or even years, thereby hindering law enforcement efforts [1]. The use of Artificial Intelligence (AI) and Large Language Models (LLMs) offers the potential to improve investigative efficiency by reducing the burden of processing massive datasets. DeepSeek-R1 is a newly released open-source LLM with advanced reasoning capabilities, achieving performance comparable to state-of-the-art models developed by OpenAI. This study outlines the role of DeepSeek-R1 in supporting digital forensic workflows—from tracing electronic evidence and analyzing data relationships to generating preliminary investigative reports. The methodology includes simulated corruption case experiments comparing investigations assisted by DeepSeek-R1 with conventional manual analysis. The results show that DeepSeek-R1 can accelerate the retrieval of relevant information and produce concise summaries rapidly, significantly reducing analysis time. However, the model remains prone to factual errors (hallucinations) and biases, making human validation by investigators essential. With proper risk mitigation and oversight, integrating DeepSeek-R1 has the potential to significantly enhance the effectiveness of digital forensic investigations in combating corruption.