Online CRISC Questions Video

Upon identification of an indicator of compromise (IoC) associated with an APT actor, the risk practitioner's best course of action is to assess the adequacy of existing security controls. This involves evaluating whether current defenses are sufficient to detect, prevent, and respond tosuch sophisticated threats. Ensuring control effectiveness is vital to mitigating the risk posed by APTs.CISA

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

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

The risk practitioner’s BEST course of action is to validate the transfer of risk and update the register to reflect the change, because outsourcing the backup and recovery procedures to a third-party cloud provider does not eliminate the risk, but rather transfers it to the service provider. The risk practitioner should verify that the service provider has adequate controls and capabilities to handle the backup and recovery procedures, and that the contractual agreement specifies the roles and responsibilities of both parties. The risk practitioner should also update the risk register to reflect the new risk owner and the residual risk level. The other options are not the best course of action, because:

Option A: Accepting the risk and documenting contingency plans for data disruption is not the best course of action, because it implies that the risk practitioner is still responsible for the risk, even though it has been transferred to the service provider. Contingency plans are also reactive measures, rather than proactive ones.

Option B: Removing the associated risk scenario from the risk register due to avoidance is not the best course of action, because it implies that the risk has been eliminated, which is not the case. The risk still exists, but it has been transferred to the service provider. The risk register should reflect the current risk status and ownership.

Option C: Mitigating the risk with compensating controls enforced by the third-party cloud provider is not the best course of action, because it implies that the risk practitioner is still involved in the risk management process, even though the risk has been transferred to the service provider. The risk practitioner should rely on the service provider’s controls and capabilities, andmonitor their performance and compliance. References = Risk and Information Systems Control Study Manual, 7th Edition, ISACA, 2020, p. 196.