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 (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
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.ConclusionsMIP-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.