Letter to the editor: Re-examining the role of deep learning in cyber forensics: Practical gaps beyond conceptual frameworks.

This letter investigates the applicability of deep learning-based techniques proposed for cyber forensics by exploring its practical limitations. Although current models depict disciplined investigation process stages, there are a lot of issues that still exist in real-world utilization. Primary concerns are data quality and bias, lack of forensic-grade validation of the method, low explainability of model outputs, and risk to evidence integrity when performing automated comparison. The debate also draws attention to the necessity of generalizing existing methodologies from post-incident review to forensic readiness and real-time monitoring. The paper contends that deep learning tools ought to serve as, not substitute for, expert judgment, with appropriate safeguards for transparency, accountability and legal admissibility. Resolving these problems is important as bridging conceptual and physical understanding with dependable forensic process.