AI Cybercrime Detection, Digital Forensics, and Governance: Scoping Review with ☸️SAIMSARA.

Name: SAIMSARA Evidence Object digital::AI_CYBERCRIME_SS
Creator: SAIMSARA
License: https://saimsara.com/license/

SAIMSARA

doi:10.62487/saimsarad6cae6d4

SAIMSARA Journal

Machine-Readable Science • ISSN 3054-3991

AI Cybercrime Detection, Digital Forensics, and Governance: Scoping Review with ☸️SAIMSARA.

Digital Health & Biotech

Issue 3, Volume 1, 2026

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DOI: 10.62487/saimsarad6cae6d4

Editorial note

• Last update: 2026-05-24 11:27:46

What is this paper about

AI cybercrime is no longer just a technical threat but a dual-use arms race: the same AI systems that scale phishing, deepfakes, fraud, and offender automation are also powering detection, forensic triage, and evidence preservation. This full evidence map shows where the strongest signals actually lie — from high-accuracy phishing and fraud detection to blockchain-backed forensics, XAI, governance gaps, and the unresolved question of whether AI-generated evidence can be trusted in real legal workflows.

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Video summary generated from this ☸️SAIMSARA evidence map. Full reference-linked paper and evidence JSON are available after purchase.

Abstract: To map and synthesize original studies addressing AI cybercrime, with emphasis on how AI is used to enable cybercrime, detect or investigate cybercrime, preserve digital evidence, support victims and law enforcement, and shape governance, adoption, and real-world risk. The review uses 162 references and builds its evidence map from 183 original studies with 61140123 total participants/sample observations (topic-deduplicated ΣN). This scoping review suggests that AI cybercrime research is dominated by a dual-use dynamic in which AI simultaneously accelerates offending and strengthens defense, with AI-powered attacks reportedly surging 238% while defensive systems achieve high study-level accuracies across phishing, fraud, and forensic tasks. The most recurrent signal indicates that AI is effective for automating detection and forensic triage, including reductions in manual investigation time and improved evidence handling through blockchain-backed integrity mechanisms. However, the evidence highlights that performance claims rest on non-comparable datasets and limited adversarial testing, so trustworthy deployment will require explainable, legally admissible, and adversarially robust systems integrated with human oversight. Practically, this supports prioritizing XAI-enabled forensic and fraud-detection tools embedded in governed workflows rather than autonomous adjudication. Future research should establish shared benchmarks and prospectively validate AI forensic outputs against courtroom admissibility and evolving offender adaptation.

Keywords: AI cybercrime; Cybercrime detection; Digital forensics; Large language models; Dark web monitoring; Adversarial attacks; Explainable AI; Blockchain evidence; Threat intelligence; Cybercrime reporting

Review Stats

Final search date and database lock: 2026-05-18 20:17:37 CEST
Plan: Pro (expanded craft tokens; source: Semantic Scholar)
Source: Semantic Scholar
Total Abstracts/Papers: 2302
Downloaded Abstracts/Papers: 1000
Included original and non-original Abstracts/Papers (all): 348
Included original Abstracts/Papers (Vote counting by direction of effect): 183
Reference Index (links used in paper): 162
Total participants/sample observations (topic deduplicated ΣN): 61140123

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Reference Index (163)

[1] Smart simulations, safer systems: agentic AI and cybercrime in E-government — https://doi.org/10.1177/00375497261429524
[2] Privacy-Preserving Cybercrime Investigation: AI and Zero-Knowledge Proofs for Secure Network Forensics — https://doi.org/10.1109/iccsp64183.2025.11088449
[3] From Rule-Based AML to Intelligent Compliance: AI-Driven, Cloud-Native Architectures for Countering Money Laundering and Cybercrime in the U.S. Financial System — https://doi.org/10.15662/ijrpetm.2025.0806033
[4] Organisational Challenges in US Law Enforcement’s Response to AI-Driven Cybercrime and Deepfake Fraud — https://doi.org/10.3390/laws14040046
[5] AI-Enhanced Network Forensic Techniques for Cybercrime Investigation Using the Gray Wolf Adaptive Forensic Anomaly Detection Algorithm (GADFADA) — https://doi.org/10.1109/incet64471.2025.11139852
[6] An AI-Driven Network Forensic Framework Using BERT and GPT for Cybercrime Investigation — https://doi.org/10.1109/cises66934.2025.11265499
[7] Exploited Intelligence: AI, Cybercrime, and the Global South’s Digital Struggle — https://doi.org/10.1177/29769442251405945
[8] Challenges in Regulating and Prosecuting AI Model Poisoning as Cybercrime — https://doi.org/10.59022/ujldp.472

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