AI in Cyber Law Enforcement and Forensic Evidence Collection

Abstract

Artificial intelligence (AI) is transforming how law-enforcement agencies investigate cybercrime and collect, analyze, and present digital evidence. This manuscript examines the end-to-end lifecycle of AI-enabled digital forensics—from first response and triage to acquisition, interpretation, legal admissibility, and courtroom presentation—through the lens of internationally recognized standards and emerging regulatory frameworks. We situate AI tools (e.g., anomaly detection for log triage, NLP for case intelligence, computer vision for media forensics, and model-agnostic explainers for transparency) within well-established forensic process models and chain-of-custody requirements. We analyze challenges unique to cloud and mobile environments, propose a standards-aligned methodology for deploying and validating AI in forensic workflows, and discuss substantive legal gatekeeping tests for expert evidence (FRE 702/Daubert/Frye) alongside the evolving constraints of the EU AI Act for law-enforcement biometric uses. A discussion of fairness, robustness, and auditability addresses risks highlighted in empirical studies (e.g., demographic performance differentials in face recognition; predictive policing feedback loops). A results section synthesizes expected operational benefits and measurable safeguards from a pilot-style implementation, while acknowledging limitations and governance needs. We conclude with a pragmatic blueprint that integrates AI capabilities without compromising evidentiary integrity, due process, or human rights, and we provide 20 authoritative references to support adoption and oversight.