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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations - AI Models, Frameworks, Strategies, and Use Cases - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training |
| Topic 2: AI Life Cycle Risk Management | 21% | - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation |
| Topic 3: AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation |
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NEW QUESTION # 54
Which control is MOST effective to prevent attackers from embedding malicious instructions within user prompts to alter model outputs?
Answer: D
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. Prompt injection is best addressed at runtime by validating incoming content and controlling model outputs. RBAC limits permissions, encryption protects stored data, and static filters may miss context- dependent attacks, while content validation directly addresses malicious instructions. This makes option D, Content validation, 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 # 55
The MOST important consideration when reviewing existing business processes to determine their suitability for AI integration is whether:
Answer: D
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. Business processes are most suitable for AI integration when objectives, decisions, and rules are sufficiently defined for the AI tool to operate against clear requirements. Industry popularity, immediate scaling, and staffing reductions do not establish suitability. This makes option D, the process includes clearly defined rules for AI tools to follow, 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 # 56
Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?
Answer: A
Explanation:
Enterprise risk framework integration elevates AI risk management from a technical discipline to a strategic organizational function, ensuring AI risks are considered alongside all other enterprise risks in strategic planning and decision-making.
Why D is Correct: The ISACA AAIR curriculum identifies enterprise integration as the mechanism that enables organization-level oversight and ensures AI risk management aligns with strategic objectives, risk appetite, and governance structures. This integration allows the board and senior management to make informed decisions about AI investment, deployment, and risk acceptance with full awareness of AI's contribution to the organizational risk profile.
Why A is Wrong: KPI benchmarking is an operational performance management activity. While integration may improve KPI accuracy, this is a secondary operational benefit rather than the primary strategic benefit of ERM integration.
Why B is Wrong: Regulatory compliance is improved by integration but represents a specific compliance benefit rather than the primary organizational value. Compliance is an output of good governance, not the purpose of ERM integration.
Why C is Wrong: Cyber threat identification is a security function that benefits from integration but is not the primary benefit. Many AI risks are non-cyber in nature-fairness, accuracy, transparency-and would not be captured by a cyber-focused framing.
NEW QUESTION # 57
Which of the following is the MOST important consideration when selecting an AI governance framework for a civil aviation system?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. Civil aviation is safety-critical, so governance must preserve qualified human oversight, intervention, escalation, and override capability. Technical simplification, privacy, and centralization are relevant but do not replace human accountability for consequential decisions. This makes option A, Provisions for human oversight, 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 # 58
Which of the following should be the PRIMARY consideration when developing AI risk awareness training?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. AI risk awareness training must remain current because threats, attack techniques, model capabilities, and regulatory expectations evolve rapidly. Static training quickly becomes obsolete, so content should be refreshed to reflect emerging AI-specific risks and attack patterns. This makes option B, Ensuring contents are regularly updated to reflect new AI threats, 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 # 59
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