A detective control is a type of internal control that seeks to uncover problems in a company's processes once they have occurred. Examples of detective controls include physical inventory checks, reviews of account reports and reconciliations, as well as assessments of current controls1. A periodic access review is a detective control that involves verifying the access rightsand privileges of users to ensure that they are appropriate and authorized. A periodic access review can help to detect any unauthorized or inappropriate access, such as excessive or redundant permissions, segregation of duties violations, or dormant ororphaned accounts23. The other options are not detective controls, but rather preventive controls, which are designed to prevent errors or fraud from occurring in the first place. A limit check is a preventive control that validates the input data against a predefined range or limit, and rejects any data that falls outside the acceptable range4. Access control software is a preventive control that restricts the access to information systems or resources based on the identity, role, or credentials of the user5. Rerun procedures are preventive controls that ensure the accuracy and completeness of data processing by repeating the same process and comparing the results6. References = Detective Control: Definition, Examples, Vs. Preventive Control; Detective Control - What Is It, Examples, Vs Preventive Control; Limit Check - an overview |ScienceDirect Topics; Access Control Software - an overview | ScienceDirect Topics; Rerun Procedures - an overview | ScienceDirect Topics

The best way to identify changes in the risk profile of an organization is to monitor key risk indicators (KRIs), which are metrics that provide information on the level of exposure to a given operational risk1. KRIs can help to monitor the changes in risk levels over time, identify emerging risks, and trigger risk response actions when the risk exceeds the acceptable thresholds2. KRIs can also help to align the risk management strategy with the business objectives and context. The other options are not the best ways to identify changes in the risk profile of an organization, as they do not provide the same level of insight and guidance as KRIs.
Monitoring key performance indicators (KPIs) may show the results or outcomes of the business processes, but not the risks or uncertainties that affect them. Interviewing the risk owner may provide some subjective or qualitative information on the risk perception or attitude, but not the objective or quantitative data on the risk exposure or impact. Conducting a gap analysis may show the difference between the current and desired state of the organization, but not the causes or sources of the risk. References = Key Risk Indicators; Key Risk Indicators: A Practical Guide

When assessing the potential risk exposure of a loss event involving personal data, the most important factor to determine is the composition and number of records in the information asset. The composition refers to the type and sensitivity of the personal data, such as name, address, phone number, email, social security number, health information, financial information, etc. The number of records refers to the quantity and scope of the personal data that is affected by the loss event. The composition and number of records in the information asset determine the severity and impact of the loss event, as they indicate the extent of the harm and damage that can be caused to the data subjects, the organization, and other stakeholders.The composition and number of records in the information asset also influence the cost of the incident response activities, the level of the regulatory fines, and the duration of the incident containment and recovery. References = CRISC Review Manual, 7th Edition, page 159.

Vulnerable firmware that lacks updates is a significant security risk, as it can be exploited by attackers.
Addressing this issue aligns with Secure IoT Deployment Practices to reduce exposure.

Masking data before being transferred to the test environment is the best recommendation to address the situation where sensitive data from the production environment is required for testing purposes in non- production environments. Data masking is a technique that replaces sensitive data elements with realistic but fictitious data, preserving the format, structure, and meaning of the original data. Data masking ensures that the test data is sufficiently anonymized and de-identified, while still maintaining its functionality and validity for testing purposes. Data masking also reduces the risk of data leakage, exposure, or breach in the test environment, which may have lower security controls than the production environment. The other options are not the best recommendations, as they do not adequately protect the sensitive data or meet the testingrequirements. Enabling data encryption in the test environment may protect the data from unauthorized access, but it does not prevent the data from being decrypted by authorized users who may misuse or mishandle it. Implementing equivalent security in the test environment may be costly, complex, or impractical, and it may not be feasible to replicate the same level of security controls as in the production environment. Preventing the use of production data for test purposes may not be possible or desirable, as production data may be required to ensure the accuracy, reliability, and quality of the testing results. References = P = NP: Cloud dataprotection in vulnerable non-production environments ...; Data masking secures sensitive data in non-production environments ...; CRISC EXAM TOPIC 2 LONG Flashcards | Quizlet