Cognitive AI Systems for Next Generation Digital Forensics and Incident Response

Abstract

Critical infrastructure cyber incidents are growing in frequency and complexity, straining traditional digital forensics and incident response (DFIR) approaches. This paper explores the integration of cognitive artificial intelligence (AI) techniques, specifically natural language processing (NLP) and computer vision into DFIR workflows. By automating evidence extraction from large volumes of text and social media, classifying malware via image analysis, and correlating multistream log data, the proposed system aims to accelerate investigations during critical infrastructure attacks. The goals are to improve detection accuracy (e.g., malware family classification and anomaly detection in logs), enhance investigators’ situational awareness through automated analytics, and significantly reduce the time needed to collect, analyze, and interpret digital evidence. The primary contributions include an architectural framework for a multi modal AI driven DFIR system, a methodology for ensuring evidentiary integrity (using hashing and blockchain), and an evaluation on representative datasets. Experimental results indicate the cognitive AI approach can boost efficiency and accuracy of forensic analysis while maintaining a strict chain of custody, thereby demonstrating the potential of AI to transform next generation DFIR in critical infrastructure contexts.