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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Risk Response and Reporting | 32% | - Risk Reporting
|
| Topic 2: IT Risk Assessment | 20% | - Risk Identification
|
| Topic 3: Governance | 26% | - Enterprise Risk Management Framework
|
| Topic 4: Monitoring and Control | 22% | - Risk Monitoring
|
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NEW QUESTION # 77
Which of the following is the BEST reason to use qualitative measures to express residual risk levels related to emerging threats?
Answer: D
Explanation:
Qualitative measures are methods of expressing risk levels using descriptive terms, such as high, medium, or low, based on subjective criteria, such as likelihood, impact, or severity. Qualitative measures are often used to identify and prioritize risks, and to communicate risk information to stakeholders1.
Residual risk is the level of risk that remains after the risk response has been implemented. Residual risk reflects the effectiveness and efficiency of the risk response, and the need for further action or monitoring2.
Emerging threats are new or evolving sources or causes of risk that have the potential to adversely affect the organization's objectives, assets, or operations. Emerging threats are often characterized by uncertainty, complexity, and ambiguity, and may require innovative or adaptive risk responses3.
The best reason to use qualitative measures to express residual risk levels related to emerging threats is that qualitative measures are better able to incorporate expert judgment. Expert judgment is the opinion or advice of a person or a group of people who have specialized knowledge, skills, or experience in a particular domain or field. Expert judgment can help to:
* Provide insights and perspectives on the nature and characteristics of the emerging threats, and their possible causes and consequences
* Assess the likelihood and impact of the emerging threats, and their interactions and dependencies with
* other risks
* Evaluate the suitability and effectiveness of the risk responses, and their alignment with the organization's risk appetite and tolerance
* Identify and recommend the best practices and lessons learned for managing the emerging threats, and for improving the risk management process45 Qualitative measures are better able to incorporate expert judgment than quantitative measures, which are methods of expressing risk levels using numerical or measurable values, such as percentages, probabilities, or monetary amounts. Quantitative measures are often used to estimate and analyze risks, and to support risk decision making1. However, quantitative measures may not be suitable or feasible for expressing residual risk levels related to emerging threats, because:
* Quantitative measures require reliable and sufficient data and information, which may not be available or accessible for the emerging threats
* Quantitative measures rely on mathematical models and techniques, which may not be able to capture or reflect the complexity and uncertainty of the emerging threats
* Quantitative measures may create a false sense of precision or accuracy, which may not be justified or warranted for the emerging threats
* Quantitative measures may be influenced or manipulated by biases or assumptions, which may not be valid or appropriate for the emerging threats67 Therefore, qualitative measures are better able to incorporate expert judgment, which can enhance the understanding and management of the residual risk levels related to emerging threats.
The other options are not the best reasons to use qualitative measures to express residual risk levels related to emerging threats, but rather some of the advantages or disadvantages of qualitative measures. Qualitative measures require less ongoing monitoring than quantitative measures, because they are simpler and easier to apply and update. However, this does not mean that qualitative measures can eliminate or reduce the need for monitoring, which is an essential part of the risk management process. Qualitative measures are better aligned to regulatory requirements than quantitative measures, because they are more consistent and comparable across different domains and contexts. However, this does not mean that qualitative measures can satisfy or comply with all the regulatory requirements, which may vary depending on the industry or sector. Qualitative measures are easier to update than quantitative measures, because they do not depend on complex calculations or formulas. However, this does not mean that qualitative measures can always reflect the current or accurate risk levels, which may change over time or due to external factors. References =
* Qualitative Risk Analysis vs. Quantitative Risk Analysis - ISACA
* Residual Risk - ISACA
* Emerging Threats - ISACA
* Expert Judgment - ISACA
* Expert Judgment in Project Management: Narrowing the Theory-Practice Gap
* Quantitative Risk Analysis - ISACA
* Quantitative Risk Analysis: A Critical Review
* [CRISC Review Manual, 7th Edition]
NEW QUESTION # 78
Which of the following will provide the BEST measure of compliance with IT policies?
Answer: B
NEW QUESTION # 79
When an information system auditor conducted a risk assessment of a company's cybersecurity architecture, they discovered several flaws related to encryption and data protection. Among these findings, which one represents the confidentiality of enterprise data?
Answer: A
Explanation:
Data transmission across public networks is the greatest risk because public networks are inherently insecure and vulnerable to interception. Encryption is critical to protecting data confidentiality during transmission over such networks. Lack of encryption internally is less risky due to controlled environments. Classification helps but does not protect data in transit. Email encryption is important but less critical compared to public network transmission risks.
NEW QUESTION # 80
A control owner responsible for the access management process has developed a machine learning model to
automatically identify excessive access privileges. What is the risk practitioner's BEST course of action?
Answer: C
Explanation:
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 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
NEW QUESTION # 81
What is the most effective approach for identifying advanced persistent threats (APTs)?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The identification of advanced persistent threats (APTs) is best supported by timely and relevant external information. Reviewing information from threat intelligence sources enables the organization to detect emerging threats quickly and accurately, reducing the mean time to identify APTs. While internal audits and KRIs are important, they typically focus on internal controls and risk monitoring rather than external threat detection. Thorough documentation of risk scenarios supports risk assessment but does not directly reduce detection time. Therefore, leveraging threat intelligence is the most effective approach for early identification of sophisticated threats.
NEW QUESTION # 82
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