article · NOUN Interdisciplinary Journal of Computing E-Learning & Application (NOUN-IJCEA)
Rapidly growing cybercrime challenges traditional digital forensics, creating an urgent need for artificial intelligence to enhance investigation speed, precision, and scalability. However, using artificial intelligence raises concerns regarding privacy, algorithmic bias, accountability, and legal admissibility. To address these issues, the AI-Driven Ethical Forensic Investigation Framework integrates machine learning, deep learning, natural language processing, explainable artificial intelligence, federated learning, differential privacy, and blockchain-based audit logs. The system is built on four theoretical pillars covering socio-technical systems, deontological ethics, data privacy, and explainability. Evaluated across 95,000 records from public, synthetic, and simulated forensic datasets, the framework attained 94.5 percent accuracy, a 92.0 percent ethical compliance score, and a 93.0 percent privacy protection score, achieving an overall performance of 93.2 percent. Comparative testing indicates that the system outperforms existing models, including traditional forensic methods, across all tested operational and ethical dimensions.
Law enforcement and forensic investigators struggle to manage massive volumes of digital evidence while preserving data privacy and legal integrity. Integrating ethical constraints, privacy protections, and explainable artificial intelligence into digital forensics ensures investigations remain accurate, accountable, and legally sound, thereby strengthening evidence admissibility and public trust in digital crime investigations.
The framework could enable software tools for cybercrime investigators, law enforcement agencies, and forensic specialists requiring ethically compliant evidence processing. The inclusion of blockchain logging, differential privacy, and explainable artificial intelligence supports regulatory and legal requirements. Given that testing has occurred across public, synthetic, and simulated datasets covering 95,000 records, the technology represents an applied research framework that has been tested in simulated environments, remaining some distance from live operational deployment.
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Cybercrime is growing at a rapid pace and it has proved that traditional digital forensic investigation approaches lack a number of critical gaps that demand the use of Artificial Intelligence (AI) to improve the efficiency, accuracy and scalability of digital investigations. The introduction of AI in forensic domains comes with concurrent challenges to issues like data privacy and confidentiality, algorithmic bias, AI ethical accountability, and legal admissibility for evidence and testimony. In this paper, the authors present the AI-Driven Ethical Forensic Investigation Framework (ADEFIF). The modular architecture is novel; includes a combination of machine learning (ML), deep learning (DL), natural language processing (NLP), explainable AI (XAI), federated learning, differential privacy, and blockchain-based audit logging as part of a digital forensic investigation system. The ADEFIF is based on four theoretic pillars: Socio-Technical Systems Theory (STST), Deontological Ethics, Data Protection and Privacy Theory, and the Explainable AI Framework (ExAI). The framework demonstrates excellent AI accuracy across all of the public, synthetic, and simulated forensic datasets, with a value of 94.5%, excellent ethical compliance score of 92.0%, and a privacy protection score value of 93.0%, resulting in an overall system performance of 93.2% across 95,000 records across the three datasets. Comparative benchmarking proves that ADEFIF surpasses all current frameworks such as Traditional Digital Forensics, Multidimensional AI Forensic Analysis Framework (MAFAF), and Privacy-by-Design AI Forensic Model (PbDAIF), in all the assessment dimensions. By incorporating investigative strategies alongside ethical considerations, privacy protections, and protocols, this framework fills a crucial space in this research niche, both by addressing the need for responsible and legally robust systems of cybercrime investigations and by serving as a blueprint for continuous refinement and innovation within AI-powered investigative frameworks.
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DOI: 10.70882/noun-ijcea.2026.1129
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