How do you select a representative sample for IT audit testing?

When considering how to choose a representative sample for IT audit testing, one typically evaluates various factors to ensure sample accurately reflects the broader population under review. Elements such as the nature & complexity of the systems in place, the significance of different processes or transactions, & the inherent risks associated with them play pivotal roles in the selection process. Additionally, understanding the objectives of the audit and the specific criteria set forth helps in identifying the most relevant data sets for testing purposes. Ultimately, the goal is to employ a methodical approach that balances efficiency with thoroughness, thereby yielding insights that contribute meaningfully to the overall audit findings.

When selecting a representative sample for IT audit testing, consider the following: Audit Sample: Method: Choose an appropriate sampling method, such as simple random sampling or systematic sampling. Size: Ensure the sample size is sufficient to provide reliable results

1-) Identify the full scope of the data or processes under review. 2-) Establish clear criteria for what the sample must represent. 3-) Select samples randomly to avoid bias and ensure fairness. 4-) Focus on high-risk areas that are critical to the IT environment. 5-) Clearly document how samples are selected and why the methodology is appropriate.

In Audit testing, there is the Test of Design commonly referred to as TOD, and Test of Effectiveness commonly referred to as TOE. As the name implies, TOD tests the design of a control. This is accomplished through walkthroughs, inquiries with control owners and performers, and walkthrough an example of one execution of the control end to end. TOE typically involves sample based testing unless the control is an automated control. TOE is typically not performed if TOD tested ineffective.

When selecting a representative sample for IT audit testing, consider the following: Audit Test: Objective: The goal is to draw conclusions about the entire population based on the sample. Risk: Be aware of sampling risk (the risk of differing conclusions due to the sample) and nonsampling risk (other factors affecting conclusions). Remember, effective communication and collaboration are essential in the audit process!

In practice, IT auditors have sampling methodologies that helps the auditor determine the right number of samples to select for testing depending on the frequency of the control (i.e., Annually, semi annually, monthly, multiple times daily, ad-hoc etc) and the audit risk for the respective testing objective. These methodology help the auditor to determine the sample population and the extent of testing that will be required to address the risk. For automated controls, the sample size can extend to 100% of the sample population.

The number one challenge in auditing a digital platform is access to data. Sampling has always been in the auditor’s toolkit as a way to predict the hypothesis that a control is effective. Verifying every transaction is an expense rarely justifiable to shareholders. But sampling is no longer practical. As the technology landscape expands and cloud adoption increases, auditors need new and improved ways to extract 100% of the data. Basing decisions on sampling methods is too big a risk.

It is important to understand that sampling didn't means everything, the purpose and depth of checking need to be considered. 5 backups found out of 5 total systems (a pass) didn't means it will always work or high likelihood to keep working. While only 3 checked out of 1000 can still draw conclusion that the 1000 are likely to work. Inexperienced auditor documents only the results, while experienced one documents why that is high likelihood. It's not a bad idea to ask for 100% compliance, but that is better achieved by having automatic checking, which can have manual compensation, instead of via result auditing only. Idea: auditee to demonstrate how the auto-control works and also catching exceptions.

If "sampling" among systemS, you're basically starting with populationS. Get less hung up on "representative" and more on "what would disprove...?"

First step is to determine whether all transactions can be extracted into a data base. If they can a program should be written to test 100 percent of the transactions. In the event it is more practical to use sampling remember you can only project to the universe of transactions by using statistical sampling procedures. I stress this because I have seen auditors selectively test 10 or 20 transactions and then render an opinion on the universe of transactions

8 - Use of Computer-Assisted Audit Techniques (CAATs) CAATs are software tools and techniques that can help you automate and enhance your audit sampling and testing procedures. Examples of CAATs include data analytics, continuous auditing, and audit management systems. These tools can enable you to analyze large volumes of data quickly and accurately, identify patterns and anomalies, and generate insights and visualizations. CAATs can also help you document your audit work, communicate with stakeholders, and monitor the implementation of recommendations. However, using CAATs requires specialized skills, resources, and controls to ensure the reliability and security of the data and the tools.

7- Risk-based Sampling In addition to the standard sampling methods, risk-based sampling is an approach that focuses on selecting items with a higher likelihood of errors, irregularities, or non-compliance. This method involves identifying and prioritizing the risk factors relevant to the audit objective, such as the complexity of the system, the volume of transactions, the history of errors, or the sensitivity of data. By concentrating on high-risk areas, you can allocate your audit resources more efficiently and effectively, and increase the chances of detecting significant issues. However, risk-based sampling may not be representative of the entire population and may require additional judgment and justification.