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The third-party contract has not been reviewed by the legal department is the auditor’s greatest concern because it poses a significant legal and financial risk to the client. A third-party contract is a legally binding agreement between the client and the outsourced payroll provider that defines the scope, terms, and conditions of the service. A third-party contract should be reviewed by the legal department to ensure that it complies with the applicable laws and regulations, protects the client’s interests and rights, and specifies the roles and responsibilities of both parties. A third-party contract that has not been reviewed by the legal department may contain clauses that are unfavorable, ambiguous, or contradictory to the client, such as:

Inadequate or unclear service level agreements (SLAs) that do not specify the quality, timeliness, and accuracy of the payroll service.

Insufficient or vague security and confidentiality provisions that do not safeguard the client’s data and information from unauthorized access, use, disclosure, or loss.

Unreasonable or excessive fees, penalties, or liabilities that may impose an undue financial burden on the client.

Limited or no audit rights that may prevent the client from verifying the effectiveness and compliance of the payroll provider’s internal controls.

Inflexible or restrictive termination clauses that may limit the client’s ability to cancel or switch to another payroll provider.

A third-party contract that has not been reviewed by the legal department may expose the client to various risks, such as:

Legal disputes or litigation with the payroll provider over contractual breaches or performance issues.

Regulatory fines or sanctions for noncompliance with tax, labor, or other laws and regulations related to payroll.

Financial losses or damages due to errors, fraud, or negligence by the payroll provider.

Reputation damage or customer dissatisfaction due to payroll errors or delays.

Therefore, an IS auditor should be highly concerned about a third-party contract that has not been reviewed by the legal department and recommend that the client seek legal advice before signing or renewing any contract with an outsourced payroll provider.

User access rights have not been periodically reviewed by the client is a moderate concern because it may indicate a lack of proper access control over the payroll system. User access rights are the permissions granted to users to access, view, modify, or delete data and information in the payroll system. User access rights should be periodically reviewed by the client to ensure that they are aligned with the user’s roles and responsibilities, and that they are revoked or modified when a user changes roles or leaves the organization. User access rights that are not periodically reviewed by the client may result in unauthorized or inappropriate access to payroll data and information, which may compromise its confidentiality, integrity, and availability.

Payroll processing costs have not been included in the IT budget is a minor concern because it may indicate a lack of proper planning and allocation of IT resources for payroll processing. Payroll processing costs are the expenses incurred by the client for using an outsourced payroll service, such as fees, charges, taxes, or penalties. Payroll processing costs should be included in the IT budget to ensure that they are adequately estimated, monitored, and controlled. Payroll processing costs that are not included in the IT budget may result in unexpected or excessive costs for payroll processing, which may affect the client’s profitability and cash flow.

The third-party contract does not comply with the vendor management policy is a low concern because it may indicate a lack of alignment between the client’s vendor management policy and its actual vendor selection and evaluation process. A vendor management policy is a set of guidelines and procedures that governs how the client manages its relationship with its vendors, such as how to select, monitor, evaluate, and terminate vendors. A vendor management policy should be consistent with the client’s business objectives, risk appetite, and regulatory requirements. A third-party contract that does not comply with the vendor management policy may result in suboptimal vendor performance or service quality, but it does not necessarily imply a breach of contract or a violation of law.

 The record-locking option of a database management system (DBMS) serves to eliminate the risk of concurrent updates to a record by different users or transactions. Record locking is a technique of preventing simultaneous access to data in a database, to prevent inconsistent results1. For example, if two bank clerks try to update the same bank account for two different transactions, record locking can ensure that only one clerk can modify the record at a time, while the other has to wait until the lock is released. This way, the record will reflect both transactions correctly and avoid data corruption.

Record locking does not serve to allow database administrators (DBAs) to record the activities of users. This is a function of auditing or logging, which can track the actions performed by users on the database2. Record locking does not affect the ability of DBAs to monitor or audit user activities.

Record locking does not serve to restrict users from changing certain values within records. This is a function of access control or authorization, which can enforce rules or policies on what data users can view or modify2. Record locking does not affect the permissions or privileges of users on the database.

Record locking does not serve to allow users to lock others out of their files. This is a function of encryption or password protection, which can secure files from unauthorized access or modification3. Record locking does not affect the security or confidentiality of files on the database.

The first step when creating a data classification program is to develop a policy (D). A data classification policy is a document that defines the purpose, scope, objectives, roles, responsibilities, and procedures of the data classification program. A data classification policy is essential for establishing the governance framework, standards, and guidelines for the data classification process. A data classification policy also helps to communicate the expectations and benefits of the data classification program to the stakeholders, such as data owners, users, custodians, and auditors12.

Categorizing and prioritizing data (A) is not the first step when creating a data classification program, but the third step. Categorizing and prioritizing data involves defining and applying the criteria and labels for classifying data based on its sensitivity, value, and risk. For example, data can be categorized into public, internal, confidential, or restricted levels. Categorizing and prioritizing data helps to identify and protect the most critical and sensitive data assets of the organization12.

Developing data process maps (B) is not the first step when creating a data classification program, but the fourth step. Developing data process maps involves documenting and analyzing the flow and lifecycle of data within the organization. Data process maps show how data is created, collected, stored, processed, transmitted, used, shared, archived, and disposed of. Developing data process maps helps to understand the context and dependencies of data, as well as to identify and mitigate any potential risks or issues related to data quality, security, or compliance12.

Categorizing information by owner © is not the first step when creating a data classification program, but the second step. Categorizing information by owner involves assigning roles and responsibilities for each type of data based on its ownership and stewardship. Data owners are the individuals or entities that have the authority and accountability for the data. Data stewards are the individuals or entities that have the operational responsibility for managing and maintaining the data. Data custodians are the individuals or entities that have the technical responsibility for implementing and enforcing the security and access controls for the data12.

The sampling method in which the entire sample is considered to be irregular if a single error is found is discovery sampling. Discovery sampling is a type of statistical sampling that is used to test for the existence of at least one occurrence of a specific characteristic or condition in a population. Discovery sampling is often used when the auditor expects the characteristic or condition to be very rare or nonexistent, and when any occurrence would have a significant impact on the audit objective. For example, discovery sampling can be used to test for fraud, noncompliance, or material misstatement.

Discovery sampling works by setting a very low tolerable error rate (the maximum rate of occurrence of the characteristic or condition that the auditor is willing to accept) and a high confidence level (the degree of assurance that the auditor wants to obtain). The auditor then selects a sample from the population using a random or systematic method, and examines each item in the sample for the presence or absence of the characteristic or condition. If no error is found in the sample, the auditor can conclude with a high level of confidence that the characteristic or condition does not exist or is very rare in the population. However, if one or more errors are found in the sample, the auditor cannot draw any conclusion about the population and must either expand the sample size or perform alternative procedures.

Discovery sampling differs from other sampling methods in that it does not allow for any errors in the sample. Other sampling methods, such as variable sampling, stop-or-go sampling, or judgmental sampling, can tolerate some errors in the sample and use them to estimate the error rate or amount in the population. However, discovery sampling is designed to test for zero-tolerance situations, where any error would be unacceptable or material. Therefore, discovery sampling considers the entire sample to be irregular if a single error is found.