Design of programming data analytic for access controls auditing
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2024
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2567
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eng
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51 leaves
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Natyapat Suwannapruk (2024). Design of programming data analytic for access controls auditing. Retrieved from: https://repository.nida.ac.th/handle/123456789/7090.
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Design of programming data analytic for access controls auditing
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Abstract
In the contemporary business landscape, technology is indispensable for growth and resilience, while data has emerged as a cornerstone asset. To effectively guide organizational development, the internal audit function must seamlessly integrate data analytics. This study delves into the significance of information technology and data analytics skills for business internal auditors and explores how data analytics tools can be integrated into audit functions, focusing on access control auditing.
The study begins by examining the roles of data analytics tools within audit functions and reviewing relevant International Internal Audit Standards and Guidelines related to data analytics. Subsequently, it analyzes Thai regulation and International Standards pertaining to access controls to design effective testing procedures for these security measures.
Building upon this literature review, the study develops a Pseudocode concept to translate traditional access control testing procedures into a series of technical steps. This allows for a demonstration of the advantages of utilizing data analytics tools for testing compared to traditional methods.
The study’s outcome includes testing flowcharts, Pseudocode designs, and an example of Python script. These demonstrate how internal auditors can leverage data analytics tools to streamline their processes and enhance both effectiveness and efficiency. However, the study acknowledges limitations, such as the technical skills required, data privacy and security concerns, and the organizational context.
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Independent Study Graduate School of Applied Statistics, 2024