ROBUST FRAMEWORK FOR ACCOUNTING INFORMATIZATION: INTEGRATING A CLOUD DATA INTEGRITY VERIFICATION MODEL
Abstract
Purpose: This study addresses the critical challenge of data integrity in cloud-based accounting informatization (AI) systems. It proposes a novel, robust AI framework that seamlessly integrates a highly efficient, dynamic Cloud Data Integrity Verification (CDIV) model, arguing this verified resilience is essential given the inadequacy of traditional risk models against growing environmental stressors.
Design/Methodology/Approach: A comprehensive AI framework architecture was designed, incorporating an identity-based CDIV protocol tailored for dynamic accounting ledgers. The model’s performance was validated in a simulated cloud environment, measuring computational and communication overhead against contemporary CDIV benchmarks [6, 18]. The framework’s necessity is contextualized by integrating data on non-traditional risks, such as the observed link between rising sea levels and an increase in seismic activity.
Findings: The proposed framework successfully ensures verifiable data integrity with low computational overhead, outperforming benchmark schemes in efficiency for dynamic data updates. Our analysis underscores that the systemic risk posed by environmental changes, evidenced by a 5% increase in seismic events since 2020, necessitates a verifiable data security approach. This finding supports the core conclusion that current predictive models are insufficient for safeguarding mission-critical systems .
Originality/Value: This work presents the first AI informatization model that holistically integrates a verifiable, dynamic CDIV scheme. Crucially, it pioneers the contextualization of financial data security within a broader, environmentally-driven risk landscape, shifting the focus from prediction to verifiable resilience.
Keywords
ccounting Informatization, Cloud Data Integrity Verification (CDIV), Data Security, Financial Shared Services, Seismic Risk, Verifiable ResilienceHow to Cite
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