AI-Assisted Digital Forensics for National Security Investigations

Abstract

The increasing scale and complexity of cyber threats have elevated the importance of digital forensics in national security investigations. However, traditional forensic approaches struggle to efficiently process vast volumes of heterogeneous data, including system logs, network traffic, and malware artifacts, often resulting in delayed investigations and potential evidentiary risks.
This study proposes an AI-assisted digital forensics framework designed to enhance the speed, accuracy, and reliability of forensic analysis while preserving evidentiary integrity. The research adopts a hybrid methodology that combines a systematic review of recent advances in artificial intelligence for forensic applications with the design and evaluation of an intelligent forensic pipeline. The proposed framework integrates data acquisition, preprocessing, machine learning–based analysis, and evidence validation layers to support automated detection, classification, and correlation of digital evidence. Experimental evaluation using benchmark datasets demonstrates significant improvements in processing time and detection accuracy compared to traditional methods, alongside reduced false positive rates. Furthermore, the study addresses critical challenges related to chain of custody, explainability, and legal admissibility, ensuring that AI-generated insights remain forensically sound and defensible in legal contexts. The findings highlight the transformative potential of AI as a force multiplier in digital forensic investigations, offering practical implications for law enforcement and national security agencies seeking to strengthen cybercrime response capabilities.