Gaps in Global Governance for AI-Assisted Cross-Border Cloud Forensics and the Imperative for the Multi-Jurisdictional Investigative Protocols for AI-Informed Digital Evidence (MIP-AIDE) Framework
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The rapid globalization of digital services has made international cloud data transfers essential, yet these processes frequently collide with divergent regional privacy regimes, such as the conflict between the U.S. CLOUD Act (Clarifying Lawful Overseas Use of Data Act) and the European Union's General Data Protection Regulation (GDPR). Current cross-border evidence acquisition relies on slow Mutual Legal Assistance Treaties (MLATs) or fragmented extraterritorial laws that often bypass data sovereignty. Furthermore, the integration of artificial intelligence (AI) in forensics introduces "black box" opacity, which threatens the admissibility of digital evidence and undermines due process. This research addresses these structural failures by proposing the MIP-AIDE framework to unify jurisdictional and AI accountability standards.
Objectives
The primary objectives are to design a tiered procedural protocol that computationally embeds international compliance rules to resolve extraterritorial conflicts; define technical standards that translate forensic AI outputs into judicial admissibility criteria, such as error rates and bias audits; and innovate a governance paradigm that integrates Cloud Service Providers (CSPs) as auditable partners in the legal process.
Methods
This project employs a mixed-method approach consisting of three phases: (I) doctrinal legal analysis and benchmarking of international standards like the Daubert and Mohan criteria; (II) a technical review of Explainable AI (XAI) techniques like SHAP and LIME; and (III) Design Science Research (DSR) to synthesize these findings into the MIP-AIDE framework.
Results
The framework delivers three core components: a Legal Gateway Decision Matrix for automated compliance checking; Minimum Technical Explanatory Requirements (MTERs) to package AI outputs into court-admissible artefacts; and a Collaborative Stewardship Model using Service Level Agreements (SLAs) to formalize the role of CSPs.
Conclusions
MIP-AIDE closes the protocolization, AI-admissibility, and non-state actor governance gaps. By providing a concrete, computable solution, the framework ensures that AI-assisted forensics achieve the speed, transparency, and legitimacy required for 21st-century digital justice.
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References
Alenezi, A. M. (2023). Digital and cloud forensic challenges. arXiv preprint arXiv:2305.03059.
ALF. (2025). Underlying Risks of Using AI-Generated Evidence in Nigeria’s Justice System. Alliance Law Firm. https://alliancelawfirm.ng/underlying-risks-of-using-ai-generated-evidence-in-nigerias-justice-system/
Amoo, O. O., Atadoga, A., Osasona, F., Abrahams, T. O., Ayinla, B. S., & Farayola, O. A. (2024). GDPR's impact on cybersecurity: A review focusing on USA and European practices. International Journal of Science and Research Archive, 11(1), 1338-1347.
Anand, S., Gopinath, A., & Periyasami, K. (2026). Evaluating Bias Detection and Mitigation Approaches Across Classical and Large Language Models. IEEE Access, 14, 35074–35095. https://doi.org/10.1109/ACCESS.2026.3669650
Bhan, S., Kumar, D. N., Singh, D. V. P., Gope, D. S., Aqib, D. M., Barman, D. P., Joshi, N. V., & Saikia, D. P. R. (2025). Challenges In Admissibility Of Forensic Evidence: A Comparative Analysis Of Legal Standards Across Jurisdictions. International Journal of Environmental Sciences, 11(14).
Chinnaraju, A. (2025). Explainable AI (XAI) for trustworthy and transparent decision-making: A theoretical framework for AI interpretability. World Journal of Advanced Engineering Technology and Sciences, 14(3), 170-207.
Chivers, W. (2019). Resisting digital surveillance reform: The arguments and tactics of communications service providers. Surveillance and Society, 17(3/4), 517-532.
De, M., & Chakraborty, D. K. (2024). Integrating Digital Forensics into Intellectual Property Rights Enforcement: A Framework for Cybercrime Investigation. Journal of Intellectual Property Rights, 29(6), 500-506.
Dou, L., & Dou, X. (2025). Towards just AI: Challenges and solution framework for algorithmic discrimination in judicial system. International Journal of Digital Law and Governance, 2(1), 39-81.
Folorunso, A., Adewa, A., Babalola, O., & Nwatu, C. E. (2024). A governance framework model for cloud computing: Role of AI, security, compliance, and management. World Journal of Advanced Research and Reviews, 24(2), 1969-1982.
Gentry, E. (2024). ‘The Challenges of Integrating AI-Generated Evidence Into the Legal System’ (Akerman LLP, 12 June 2024) https://www.akerman.com/en/perspectives/the-challenges-of-integrating-ai-generated-evidence-into-the-legal-system.html.
Giardiello, G., & Turchi, F. (2023). Evidence Exchange Standard Package: An Application CASE Ontology Complied for the Preparation of the Evidence Package and Its Exchange. In M. A. Biasiotti & F. Turchi (Eds.), European Investigation Order: Where the Law Meets the Technology (pp. 97–126). Springer International Publishing. https://doi.org/10.1007/978-3-031-31686-9_8
LATHER, R., SINGH, D. R., NEHRA, V., & JAIN, S. (2025). Cyber Shield & Scales: Legal Strategy for Digital Defense 2025. Yashita Prakashan Private Limited.
Macturk, E. L., Hayes, K., O'Sullivan, G., & Perrault Uptmor, K. A. (2025). Are We Ready for It? A Review of Forensic Applications and Readiness for Comprehensive Two‐Dimensional Gas Chromatography in Routine Forensic Analysis. Journal of Separation Science, 48(4), e70138.
Md Fokrul Islam Khan. (2025). Risk Management Framework in the AI Act. International Journal of Science and Research Archive, 14(3), 466–471. https://doi.org/10.30574/ijsra.2025.14.3.0688
Nath, S., Summers, K., Baek, J., & Ahn, G. J. (2024, October). Digital evidence chain of custody: Navigating new realities of digital forensics. In 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA) (pp. 11-20). IEEE.
Nsor, M., & Bakare, F. A. (2025). Leveraging big data engineering techniques for automated evidence extraction and pattern recognition in cybercrime forensic analysis. World Journal of Advanced Research and Reviews, 27(1), 2532–2553. https://doi.org/10.30574/wjarr.2025.27.1.2818
Olber Dr, P. (2021). The survey on cross-border collection of digital evidence by representatives from Polish prosecutors’ offices and judicial authorities. Journal of Digital Forensics, Security and Law, 16(2), 3.
Orji, N. A. (2024). Admissibility of AI-generated Evidence under the Nigerian Evidence Act, 2023. Available at SSRN 5035075.
Patel, D. G. (2025). Securing Cloud Infrastructure Through Ancestry Tracking in Machine Images. IJSAT-International Journal on Science and Technology, 16(3).
Perault, M., & Salgado, R. (2024). Untapping the Full Potential of CLOUD Act Agreements. Center for Strategic and International Studies (CSIS).
Patterson, T. (2025). Cross-Border Cloud Data Transfers: Case Studies on Balancing US Government Privacy Regulations with Global Business Needs. Available at SSRN 5383863.
Okunrobo Perez, S. (2025). Proliferation of e-Evidence: Reliability Standards and the Right to a Fair Trial. European Journal of Crime, Criminal Law and Criminal Justice, 33(1-2), 187-211. https://doi.org/10.1163/15718174-bja10070.
Ucar, M., & Yalcintas, A. (2023). GDPR and Digital Protectionism in the EU: The Cases of Android and iOS. Journal of Economic Issues, 57(4), 1079–1094. https://doi.org/10.1080/00213624.2023.2273120.
Voigt, P., & Von dem Bussche, A. (2024). The EU General Data Protection Regulation (GDPR): A Practical Guide (Springer Nature Switzerland 2024) https://link.springer.com/10.1007/978-3-031-62328-8
Zharova, A. K. (2023). Achieving algorithmic transparency and managing risks of data security when making decisions without human interference: legal approaches. Journal of Digital Technologies and Law, 1(4), 973-993.
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