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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| Topic 2: AI Life Cycle Risk Management | 21% | - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Design, Development/Procurement, and Documentation |
| Topic 3: AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting |
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NEW QUESTION # 88
A hospital is deploying an AI system to build personalized treatment profiles for patients. The risk practitioner reviews the privacy and legal risks of this AI initiative. Which item represents the **most significant legal and privacy risk?
Answer: A
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. Using patient information for personalized AI treatment profiles without consent or another valid authorization creates significant privacy, legal, ethical, and trust exposure. SSO and synthetic-data use do not address this fundamental lawful-processing requirement. This makes option D, There is no consent for the organization to use the patients ' information, 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 # 89
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?
Answer: B
Explanation:
Third-party LLMs process organizational data-including sensitive and proprietary information-during both training and inference. The vendor's data handling practices determine whether the organization's data remains private, secure, and compliant with legal obligations.
Why D is Correct: According to ISACA AAIR third-party risk guidance, data handling practices are the most critical evaluation criterion for AI vendors. How the vendor uses input data-whether for model training, analytics, or retention-directly determines data privacy risk, intellectual property exposure, and regulatory compliance. Vendors who train on customer input data without restriction create significant privacy and confidentiality risks.
Why A is Wrong: SLA alignment with corporate strategy addresses availability and performance obligations.
While important, these commercial terms do not address the fundamental data risk created by vendor data handling practices.
Why B is Wrong: ML method selection reflects technical sophistication but does not determine data risk. The risk profile is driven by data governance, not algorithmic choice.
Why C is Wrong: Subscription models represent commercial and procurement considerations. Pricing structure has no bearing on data privacy risk or the organization's risk exposure from vendor data practices.
NEW QUESTION # 90
To reinforce organization-wide ethical norms and risk recognition, which of the following is MOST important to integrate into AI user training?
Answer: B
Explanation:
Effective AI user training must go beyond policy acknowledgment and compliance instruction to equip employees with the practical skills needed to identify ethical risks and report them appropriately. This builds an active risk-aware workforce.
Why B is Correct: The ISACA AAIR framework identifies that training on ethical risk indicators and reporting mechanisms directly reinforces ethical norms by enabling employees to recognize real-world signs of AI misuse, bias, or harmful outputs. When staff can identify specific risk signals and know how to escalate them, the organization builds a proactive risk culture grounded in practical ethical literacy.
Why A is Wrong: Acceptable use policy acknowledgment is a compliance activity, not a culture-building measure. Acknowledging a document does not ensure employees understand how to apply ethical principles in practice.
Why C is Wrong: Cyber threat identification addresses security risk, which is narrower than the full scope of ethical AI risk. Security training does not develop ethical judgment regarding fairness, bias, or societal impact.
Why D is Wrong: Regulatory compliance checklists address legal obligations but do not develop the ethical reasoning and risk recognition skills needed to reinforce organizational norms.
NEW QUESTION # 91
Which of the following poses the GREATEST challenge related to the protection of intellectual property generated by AI solutions?
Answer: A
Explanation:
Traditional intellectual property law was designed for human-created works. AI-generated content sits in a legal grey zone because current copyright frameworks in most jurisdictions do not clearly establish who-if anyone-holds copyright in outputs created autonomously by AI systems.
Why C is Correct: According to ISACA AAIR, the lack of regulatory clarity around AI-generated content copyright is the greatest IP challenge because it creates fundamental uncertainty about ownership, transferability, and enforceability of rights in AI outputs. Without clear legal status, organizations cannot confidently assert ownership, license AI-generated materials, or prevent competitors from copying outputs.
This uncertainty pervades commercial agreements, licensing strategies, and competitive protection.
Why A is Wrong: Zero-data retention policies actually protect intellectual property by ensuring vendor systems do not retain proprietary input data. This represents a protective measure, not a challenge.
Why B is Wrong: Training material customization for confidential data handling is a workforce education challenge. While important for data protection, it does not represent the primary IP challenge from AI- generated content.
Why D is Wrong: Low-risk use cases like administrative tasks present minimal IP concerns because the outputs are typically not commercially significant or protectable. The IP challenge is greatest for creative, analytical, and proprietary outputs.
NEW QUESTION # 92
Which of the following is the MOST important consideration when developing AI security policies?
Answer: A
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 security policies should be relevant to the organization ' s current and planned use cases and strategic objectives. A policy disconnected from actual AI adoption will not establish useful requirements for the risks the organization is taking. This makes option B, Relevance to current and projected AI use cases and strategic objectives, 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 # 93
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