Authors: Anura J. Perera
Abstract: Artificial intelligence (AI) has revolutionized post-incident investigations by introducing automation, precision, and predictive intelligence into digital forensic processes. Traditional forensic techniques, dependent on manual log examination and human expertise, often struggle to handle the enormous data volumes and complex digital trails generated in modern cyber incidents. AI-based forensic systems leverage machine learning, natural language processing, and data mining to extract, analyze, and correlate evidence at unprecedented speed and accuracy. These systems can automatically detect anomalies, reconstruct attack timelines, and identify threat actors by learning from historical data patterns. The review explores the evolution of digital forensics, the integration of AI-driven tools, and the measurable improvements in investigation efficiency. It also highlights challenges such as model transparency, bias, explainability, and legal admissibility of AI-generated evidence. Moreover, it discusses emerging research opportunities, including explainable AI, blockchain-enabled evidence validation, and collaborative forensics through federated learning. By synthesizing academic research and industrial applications, this paper emphasizes that AI-based forensic systems are not merely tools but strategic enablers of resilience in cyber incident response. They represent a paradigm shift toward intelligent, adaptive, and self-learning investigation frameworks, crucial for addressing the growing sophistication of digital threats in enterprise and law enforcement environments.
DOI: http://doi.org/10.5281/zenodo.17880644
