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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 2: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 3: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 4: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 5: AI Lifecycle Controls | - Controls across AI development lifecycle
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NEW QUESTION # 34
Which of the following is the GREATEST benefit of incorporating AI technology for data asset management?
Answer: B
Explanation:
Data asset management for large-scale AI programs involves processing, cataloging, and maintaining vast quantities of structured and unstructured data. AI-powered automation addresses the scalability challenges of manual data management processes.
Why D is Correct: The ISACA AAIR AI capabilities guidance identifies automating data cleaning and metadata tagging as the greatest practical benefit of AI-powered data asset management. Large datasets- often containing millions of records-require consistent preprocessing and cataloging to be usable for AI training and governance. AI automation achieves this at scale, with speed and consistency that manual processes cannot match, improving data quality and discoverability across the organization.
Why A is Wrong: Justifying synthetic data usage is a model development strategy decision, not a data asset management benefit. The justification for synthetic data depends on use case requirements, not AI automation capability.
Why B is Wrong: AI tools can support security monitoring but do not inherently reduce the initial impact of data poisoning or exfiltration attacks. Security outcomes depend on specific defensive AI applications, not general data management automation.
Why C is Wrong: Overfitting identification during model training is a model development monitoring activity. While AI can support training analytics, this is a narrow benefit compared to the broad, scalable data asset management value of automated cleaning and tagging.
NEW QUESTION # 35
A risk practitioner discovers that an AI chatbot stores user interactions indefinitely. Which of the following is the MOST appropriate mitigation?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Indefinite chatbot retention creates unnecessary privacy and breach exposure. Automated retention limits linked to documented consent or purpose ensure records are kept only as long as justified while still accommodating legitimate legal, operational, or audit requirements. This makes option B, Automate retention limits linked to documented consent, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 36
An organization deploys an autonomous system that makes decisions affecting compliance with regulations.
If those decisions could potentially produce regulatory breaches, which of the following BEST helps to manage associated liability exposures?
Answer: B
Explanation:
Liability from autonomous AI decisions affecting regulatory compliance requires organizations to demonstrate accountability, oversight, and control. Documentation of decision rationale and embedded oversight controls are the primary mechanisms for demonstrating responsible governance to regulators.
Why B is Correct: The ISACA AAIR framework identifies explainability documentation and embedded oversight controls as the key liability management tools for autonomous AI systems. When the organization can demonstrate that each AI decision was explainable, that controls were in place to detect violations, and that human oversight was embedded in the process, this demonstrates due diligence-which is the legal and regulatory standard for managing liability from automated decisions.
Why A is Wrong: Separate compliance programs fragment governance and may increase rather than reduce liability by suggesting AI compliance is siloed from the enterprise compliance program. Regulators expect integrated governance.
Why C is Wrong: Restricting deployment represents risk avoidance, not liability management for already- deployed systems. If the system is already in production, deployment restriction does not address existing liability.
Why D is Wrong: Single-point escalation and non-disclosure create governance bottlenecks and conflict with regulatory transparency requirements. Restricting disclosure cannot be used to shield the organization from regulatory accountability for automated decisions.
NEW QUESTION # 37
Which of the following is the BEST approach when using AI-driven security ratings to monitor third-party risk?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Third-party security ratings are useful only when they support business and compliance decisions.
Mapping rating metrics to strategic objectives and obligations turns technical measures into meaningful risk information instead of isolated performance or availability indicators. This makes option C, Map risk metrics to strategic objectives and compliance obligations, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk- management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 38
Which of the following information is MOST important to add to an organizational business continuity plan (BCP) when adopting a customer-facing AI solution?
Answer: D
Explanation:
Business continuity planning for customer-facing AI solutions must ensure service availability and resilience under failure conditions. The BCP must specify the technical and operational mechanisms that maintain service continuity when primary systems are disrupted.
Why B is Correct: The ISACA AAIR business continuity guidance identifies secure access to alternate resources, multi-region failover, and load balancing as the most important additions to a BCP for customer- facing AI. These mechanisms ensure that service disruptions-whether from technical failures, cyber incidents, or regional outages-do not result in total unavailability. For customer-facing solutions, maintaining service continuity directly affects customer trust, revenue, and regulatory compliance with service availability obligations.
Why A is Wrong: Post-incident audits of recovery times and accuracy metrics are monitoring activities that occur after incidents. While valuable for improvement planning, they do not define the recovery mechanisms that the BCP must specify to ensure continuity during disruptions.
Why C is Wrong: Centralizing failover under a single cloud provider creates a concentration risk-if that provider experiences an outage, all failover mechanisms fail simultaneously. Good BCP design requires geographic and provider diversification, not concentration.
Why D is Wrong: Breach containment criteria address security incident response, not service continuity.
While related to incident management, breach response procedures are typically documented in the incident response plan rather than the BCP, which focuses on maintaining or restoring business operations.
NEW QUESTION # 39
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