CRISC Exam Dumps - Certified in Risk and Information Systems Control

 The business application owner should have the authority to accept the associated risk, because they are responsible for the performance and outcomes of the critical application, and they understand the business requirements, expectations, and impact of the application. The business application owner can also evaluate the trade-offs between the potential benefits and costs of the application, and the potential risks and consequences of a disruption or failure of the application. The business application owner can also communicate and justify their risk acceptance decision to the senior management and other stakeholders, and ensure that the risk is monitored and reviewed regularly. The other options are less appropriate to have the authority to accept the associated risk. The business continuity director is responsible for overseeing the planning and execution of the business continuity strategy, which includes ensuring the availability andresilience of the critical business processes and applications. However, they are not the owner of the application, and they may not have the full knowledge or authority to accept the risk on behalf of the business. The disaster recovery manager is responsible for managing the recovery and restoration of the IT systems and applications in the event of a disaster or disruption. However, they are not the owner of the application, and they may not have the full knowledge or authority to accept the risk on behalf of the business. The data center manager is responsible for managing the operation and maintenance of the data center infrastructure, which includes providing the physical and environmental security, power, cooling, and network connectivity for the IT systems and applications. However, they are not the owner of the application, and they may not have the full knowledge or authority to accept the risk on behalf of the business. References = Risk IT Framework, ISACA, 2022, p. 181

A heat map is a graphical representation of the organization’s risk profile that shows the relative level of risk for each risk category or event. A heat map uses colors, shapes, or symbols to indicate the magnitude and likelihood of each risk, as well as its trend and status. A heat map offers the simplest overview of changes in the organization’s risk profile, as it allows the risk decision-makers to quickly identify the most significant risks, theareas of improvement or deterioration, and the gaps or overlaps in risk management. A heat map can also be used to communicate the risk profile to senior management and other stakeholders in a clear and concise manner. References = Risk and Information Systems Control Study Manual, Chapter 2: IT Risk Assessment, Section 2.3: IT Risk Assessment Methods and Techniques, Page 77; Future Risks: How organizations see changes in risk management - Aon.

Managing cyber risk according to the organization’s risk management framework is the best recommendation to help ensure cyber risk is assessed and reflected in the enterprise-level risk profile, as it helps to integrate and align the cybersecurity risk management (CSRM) and the enterprise risk management (ERM) processes. A risk management framework is a set of principles, policies, and practices that guide and support the risk management activities within an organization. A risk management framework helps to establish a consistent, comprehensive, and coordinated approach to risk management across the organization and to the external stakeholders.

Managing cyber risk according to the organization’s risk management framework helps to ensure cyber risk is assessed and reflected in the enterprise-level risk profile by providing the following benefits:

It enables a holistic and comprehensive view of the cyber risk landscape and its interdependencies with the business processes and functions.

It facilitates the communication and collaboration among the business and IT stakeholders and enhances their understanding and awareness of the cyber risk exposure and control environment.

It supports the development and implementation of effective and efficient cyber risk response and mitigation strategies and actions that are aligned with the business risk appetite and objectives.

It provides feedback and learning opportunities for the cyber risk management and control processes and helps to foster a culture of continuous improvement and innovation.

The other options are not the best recommendations to help ensure cyber risk is assessed and reflected in the enterprise-level risk profile. Defining cyber roles and responsibilities across the organization is a good practice to clarify and assign the duties and accountabilities for the cyber risk management and control processes, but it does not directly address the cyber risk assessment and integration with the enterprise-level risk profile. Conducting cyber risk awareness training tailored specifically for senior management is a useful method to educate and engage the senior management in the cyber risk management and control processes, but it does not provide asystematic or consistent way to assess and reflect the cyber risk in the enterprise-level risk profile. Implementing a cyber risk program based on industry best practices is a possible action to improve and enhance the cyber risk management and control processes, but it does not ensure the alignment or integration with the organization’s risk management framework or the enterprise-level risk profile. References = Integrating Cybersecurity and Enterprise Risk Management (ERM) - NIST, IT Risk Resources | ISACA, Identifying and Estimating Cybersecurity Risk for Enterprise Risk …

Modifying logs before analysis compromises the integrity and reliability of monitoring processes. This action creates a risk of inaccurate data feeding into key risk indicators, which undermines the effectiveness of monitoring and decision-making. Maintaining log integrity is a foundational practice inRisk Monitoring and Reporting.

 A business impact analysis (BIA) is a process that identifies and evaluates the potential effects of a disaster on the critical functions and processes of an organization1. A BIA helps to forecast the operational, financial, legal, and reputational impacts of a disaster, as well as the recovery priorities and resources needed to resume normal operations2. A BIA also helps to determine the recovery time objectives (RTO), which are the maximum acceptable time frames for restoring the critical functions and processes after a disaster3. Therefore, developing a BIA is the most important step for an organization to forecast the effects of a disaster and plan for its recovery. Defining RTOs is a part of the BIA process, not a separate activity. Analyzing capability maturity model gaps is a method to assess the effectiveness and efficiency of the organization’s processes and practices, but it does not directly forecast the effects of adisaster4. Simulating a disaster recovery is a way to test and validate the recovery plans and procedures, but it does not forecast the effects of a disaster either5. References = Risk and Information Systems Control Study Manual, Chapter 5: Risk Response and Mitigation, Section 5.3: Business Continuity Planning, pp. 227-238.

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 riskmanagement 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 orrecommendations 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

Risk tolerance is the amount and type of risk that an organization is willing to accept in pursuit of its objectives. Risk tolerance is an important component of the corporate risk profile, as it defines the boundaries and limits of the acceptable risk exposure for the organization. Comparing the risk tolerance against the corporate risk profile can help to ensure that the organization’s risk strategy and objectives are aligned with its risk appetite and capacity, and that the organization is not taking on more risk than it can handle or afford. Comparing the risk tolerance against the corporate risk profile can also help to monitor and adjust the risk management process and controls, and to optimize the risk-return trade-off. References = ISACA Certified in Risk and Information Systems Control (CRISC) Certification Exam Question and Answers, Question 249. CRISC: Certified in Risk & Information Systems Control Sample Questions, Question 249. CRISC Sample Questions 2024, Question 249. CRISC by Isaca Actual Free Exam Q&As, Question 9.

The control owner is the person who is responsible for ensuring that the control is designed to effectively address risk. The control owner is also responsible for implementing, operating, monitoring, and maintaining the control. The control owner should ensure that the control is aligned with the risk owner’s risk appetite and tolerance, and that the control is periodically reviewed and updated to reflect changes in the risk environment. The risk manager, the control tester, and the risk owner are not directly responsible for the design of the control, although they may provide input, feedback, or approval. References = Risk and Information Systems Control Study Manual, Chapter 1, Section 1.3.2, page 1-15.