End-user computing (EUC) is a system in which users are able to create working applications besides the divided development process of design, build, test and release that is typically followed by software engineers1. Examples of EUC tools include spreadsheets, databases, low-code/no-code platforms, and generative AI applications2. EUC tools can provide flexibility, efficiency, and innovation for the users, but they also pose significant risks if not properly managed and controlled3.
The greatest risk when relying on reports generated by EUC is that the data may be inaccurate. Data accuracy refers to the extent to which the data in the reports reflect the true values of the underlying information4.
Inaccurate data can lead to erroneous decisions, misleading analysis, unreliable reporting, and compliance violations. Some of the factors that can cause data inaccuracy in EUC reports are:
Lack of rigorous testing: EUC tools may not undergo the same level of testing and validation as IT-developed applications, which can result in errors, bugs, or inconsistencies in the data processing and output3.
Lack of version and change control: EUC tools may not have a clear record of the changes made to them over time, which can create confusion, duplication, or loss of data. Users may also modify or overwrite the data without proper authorization or documentation3.
Lack of documentation and reliance on end-user who developed it: EUC tools may not have sufficient documentation to explain their purpose, functionality, assumptions, limitations, and dependencies. Users may also rely on the knowledge and expertise of the original developer, who may not be available or may not have followed best practices3.
Lack of maintenance processes: EUC tools may not have regular updates, backups, or reviews to ensure their functionality and security. Users may also neglect to delete or archive obsolete or redundant data3.
Lack of security: EUC tools may not have adequate access controls, encryption, or authentication mechanisms to protect the data from unauthorized access, modification, or disclosure. Users may also store or share the data in insecure locations or devices3.
Lack of audit trail: EUC tools may not have a traceable history of the data sources, inputs, outputs, calculations, and transformations. Users may also manipulate or falsify the data without detection or accountability3.
Overreliance on manual controls: EUC tools may depend on human intervention to input, verify, or correct the data, which can introduce errors, delays, or biases. Users may also lack the skills or training to use the EUC tools effectively and efficiently3.
The other options are not as great as data inaccuracy when relying on EUC reports. Reports may not work efficiently, reports may not be timely, and historical data may not be available are all potential risks associated with EUC tools, but they are less severe and less frequent than data inaccuracy. Moreover, these risks can be mitigated by improving the performance, scheduling, and storage of the EUC tools. However, data inaccuracy can have a pervasive and lasting impact on the quality and credibility of the reports and the decisions based on them. Therefore, option A is the correct answer.
References:
What is Data Accuracy?
What Is End User Computing (EUC) Risk?
End-user computing
End-User Computing (EUC) Risks: A Comprehensive Guide

Separate authorization for input of transactions. This control would have best prevented this type of fraud in a retail environment by ensuring that the warehouse employee who handles the inventory items does not have the authority to enter adjustments to the inventory system. This would create a segregation of duties that would reduce the risk of collusion and concealment of theft.
The other options are not as effective as option A in preventing this type of fraud. Option B, statistical sampling of adjustment transactions, is a detective control that may help identify fraudulent transactions after they have occurred, but it does not prevent them from happening in the first place. Option C, unscheduled audits of lost stock lines, is also a detective control that may reveal discrepancies between the physical and recorded inventory, but it does not address the root cause of the fraud. Option D, an edit check for the validity of the inventory transaction, is a preventive control that may help verify the accuracy and completeness of the transaction data, but it does not prevent unauthorized or fraudulent adjustments.
References:
* ISACA, CISA Review Manual, 27th Edition, 2019
* ISACA, CISA Review Questions, Answers & Explanations Database - 12 Month Subscription
* Different Types of Inventory Fraud and How to Prevent Them1
* 6 Ways to Prevent Inventory Fraud in Your Business2