Abstract

The exponential data volume, advanced cyber-crimes, and complicated technologies have challenged digital forensics as never before. The conventional methods of forensics have not been able to cope with a batch of files numbering in millions, creating processing backlogs of 6-12 months. The financial obstacles to commercial forensic tools are extremely high, including licensing fees of up to $20,000 per year, and manual analysis omits an estimated quarter of important evidence. The solution is Artificial Intelligence that will allow automated triage of evidence, smart pattern recognition, and a simplified investigation process. Machine learning and deep learning models are used to classify evidence with 90-97% accuracy and consume 60-80% less time. Explainable AI methods such as SHAP and LIME provide a transparent way to make decisions, which is especially essential for legal admissibility. The paper provides an extensive literature review on AI applications in the areas of digital forensics, evaluates implementation issues, and discusses future research perspectives in creating understandable, efficient, and legally acceptable AI-based forensic systems.