Isaca Updated CRISC Exam Questions and Answers by morgan

Residual risk is the risk that remains after the risk mitigation plans have been implemented. Residual risk reflects the effectiveness of the risk response in reducing the likelihood or impact of the risk. The best evidence that risk mitigation plans have been implemented effectively is the change in the level of residual risk. A change in the level of residual risk can be measured by comparing the risk level before and after the risk mitigation plans have been executed. A change in the level of residual risk can also be evaluated by comparing the actual residual risk with the target or acceptable residual risk. A change in the level of residual risk can demonstrate how well the risk mitigation plans have achieved the risk objectives and met the risk criteria. A change in the level of residual risk can also provide feedback and lessons learned for future risk management activities. References = Residual Risk: Definition, Formula & Management, Residual Risk: What It Is and How to Manage It, Residual Risk: How to Calculate and Manage It.

 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 testing requirements. 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 data protection in vulnerable non-production environments …; Data masking secures sensitive data in non-production environments …; CRISC EXAM TOPIC 2 LONG Flashcards | Quizlet

Ineffective control implementation can result in increased risk exposure, reduced compliance, and diminished performance for the organization. Therefore, the most relevant information for stakeholders is the impact of ineffective control implementation on the business objectives, processes, and outcomes. The impact on business can include financial losses, reputational damage, operational inefficiencies, customer dissatisfaction, and legal liabilities. The other options are not as relevant as the impact on business, because they do not directly link the control effectiveness to the business value. References = Risk and Information Systems Control Study Manual, Chapter 4, Section 4.3.2, page 128.

The risk practitioner’s best course of action is to review the design of the machine learning model against the control objectives, because this will help to evaluate the suitability, effectiveness, and reliability of the model as a control measure. A machine learning model is a type of artificial intelligence that can learn from data and make predictions or decisions based on the data. A machine learning model can be used to automate or enhance the access management process, such as by identifying excessive access privileges, detecting unauthorized access, or recommending access rights. However, a machine learning model also introduces new risks and challenges, such as data quality, model accuracy, model bias, model explainability, model security, and model governance. Therefore, the risk practitioner should review the design of the machine learning model against the control objectives, which are the specific goals or outcomes that the control is intended to achieve. The control objectives can be derived from the IT risk management strategy, the IT governance framework, the IT policies and standards, and the regulatory requirements. The review of the machine learning model should cover the following aspects: - The data sources and inputs: The risk practitioner should verify that the data used to train and test the machine learning model is relevant, complete, accurate, consistent, and representative of the access management process and the access rights. The risk practitioner should also check that the data is collected, stored, processed, and transmitted in a secure and compliant manner, and that the data privacy and confidentiality are protected. - The model algorithms and outputs: The risk practitioner should validate that the model algorithms are appropriate, robust, and transparent for the access management process and the control objectives. The risk practitioner should also evaluate that the model outputs are accurate, reliable, and interpretable, and that they provide meaningful and actionable insights or recommendations for the access management process and the control objectives. - The model performance and monitoring: The riskpractitioner should measure and monitor the model performance and effectiveness against the control objectives and the predefined metrics and indicators. The risk practitioner should also ensure that the model is updated and maintained regularly to reflect the changes in the access management process and the access rights, and that the model is audited and reviewed periodically to ensure its compliance and quality. By reviewing the design of the machine learning model against the control objectives, the risk practitioner can ensure that the model is fit for purpose and adds value to the access management process and the control objectives. The risk practitioner can also identify and mitigate any potential risks or issues that may arise from the use of the machine learning model as a control measure. References = Risk and Information Systems Control Study Manual, Chapter 3: Risk Response and Mitigation, Section 3.3: Control Design and Implementation, pp. 124-1271, Manage roles in your workspace - Azure Machine Learning2, Dataset Inference: Ownership Resolution in Machine Learning3