Chances and Challenges of the Model Context Protocol in Digital Forensics and Incident Response

TL;DR

This paper investigates how the Model Context Protocol can enhance transparency, explainability, and reproducibility in digital forensics using large language models, proposing a framework for integration and discussing potential challenges.

Contribution

It introduces the concept of the inference constraint level and analyzes MCP's integration across forensic scenarios, offering a theoretical foundation for its adoption in digital forensics.

Findings

MCP can improve forensic workflow transparency and reproducibility.

The inference constraint level enhances auditability and traceability.

MCP facilitates broader application of LLMs in digital forensics.

Abstract

Large language models hold considerable promise for supporting forensic investigations, but their widespread adoption is hindered by a lack of transparency, explainability, and reproducibility. This paper explores how the emerging Model Context Protocol can address these challenges and support the meaningful use of LLMs in digital forensics. Through a theoretical analysis, we examine how MCP can be integrated across various forensic scenarios - ranging from artifact analysis to the generation of interpretable reports. We also outline both technical and conceptual considerations for deploying an MCP server in forensic environments. Our analysis reveals a wide range of use cases in which MCP not only strengthens existing forensic workflows but also facilitates the application of LLMs to areas of forensics where their use was previously limited. Furthermore, we introduce the concept of the inference constraint level - a way of characterizing how specific MCP design choices can deliberately constrain model behavior, thereby enhancing both auditability and traceability. Our insights demonstrate that MCP has significant potential as a foundational component for developing LLM-assisted forensic workflows that are not only more transparent, reproducible, and legally defensible, but also represent a step toward increased automation in digital forensic analysis. However, we also highlight potential challenges that the adoption of MCP may pose for digital forensics in the future.