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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 2: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 3: AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Topic 4: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| Topic 5: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
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NEW QUESTION # 24
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
Answer: D
Explanation:
Model poisoning attacks target the training data or model parameters to degrade performance or introduce malicious behavior. Supply chain tampering introduces compromised components at vendor or integration stages. Security by design principles require embedding defenses against these threats from the earliest design stages.
Why C is Correct: According to ISACA AAIR security by design guidance, adversarial resilience and data integrity controls address both model poisoning and supply chain tampering at their root. Adversarial resilience training prepares the model to resist maliciously crafted inputs. Data integrity controls- cryptographic signing, provenance tracking, integrity verification-detect tampering in training data and model artifacts across the supply chain. Together, these form the most comprehensive defense against both attack categories.
Why A is Wrong: Data refreshes with checksums detect post-hoc data corruption but do not build adversarial resilience into the model itself. Checksums verify file integrity but cannot prevent poisoning attacks that maintain file integrity while altering data content.
Why B is Wrong: Frequent retraining and bias monitoring address performance drift and fairness but do not specifically protect against deliberate tampering. A retrained model may still be trained on poisoned data if integrity controls are absent.
Why D is Wrong: Data tokenization protects sensitive field values from unauthorized access (a privacy control) but does not address model poisoning or supply chain tampering, which can occur without accessing or exposing the sensitive field values themselves.
NEW QUESTION # 25
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?
Answer: D
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 # 26
An organization depends on multiple external suppliers for AI models and training datasets. Which of the following is MOST important to have in place in order to reduce supply chain risk?
Answer: A
Explanation:
AI supply chain risk arises when external models or datasets are tampered with, have undisclosed characteristics, or cannot be traced to trusted origins. End-to-end provenance and audit trails address these risks by enabling verification of integrity and origin at every stage of the supply chain.
Why A is Correct: According to ISACA AAIR supply chain risk management guidance, verifiable provenance and audit trails are the most important supply chain protection mechanism. Provenance documentation traces the origin, handling, and transformation history of every externally sourced AI artifact- enabling the organization to verify that models and datasets have not been tampered with, that data sources are legitimate, and that the supply chain has not been compromised. Without provenance, organizations cannot distinguish trustworthy from compromised artifacts.
Why B is Wrong: Indemnity clauses assign financial liability after harm occurs. They provide legal recourse but do not prevent supply chain attacks or help the organization verify artifact integrity before deployment.
Why C is Wrong: Training method documentation provides useful technical context but does not verify that the actual artifacts delivered match the documentation. Documentation can be falsified; provenance verification with cryptographic integrity checks cannot.
Why D is Wrong: A vendor risk manager provides governance oversight and relationship management. While important for managing vendor relationships, a single contact point does not substitute for technical provenance verification of every artifact in the supply chain.
NEW QUESTION # 27
An organization plans to procure an AI model from a third-party supplier for a critical business function.
Which of the following is MOST important to evaluate during supplier vetting?
Answer: D
Explanation:
AI model procurement for critical business functions requires that the selected model be fit for purpose. An AI model that does not align with the specific use case creates performance, compliance, and risk management failures regardless of its technical sophistication.
Why A is Correct: ISACA AAIR procurement guidance emphasizes use case alignment as the primary vetting criterion. A model optimized for one domain may perform poorly, introduce bias, or generate inaccurate outputs in a different context. For critical business functions, misalignment directly translates to operational risk, decision errors, and potential harm. Use case fit determines whether all other evaluation criteria are even relevant.
Why B is Wrong: Dataset size is a technical characteristic that may indicate breadth of training but does not determine suitability for a specific use case. A large general-purpose dataset may be less relevant than a smaller, domain-specific one.
Why C is Wrong: Industry certifications validate security controls and quality management processes. While useful supplementary evidence, they do not confirm that a model performs appropriately for the organization's specific application.
Why D is Wrong: Emphasis on innovation reflects vendor marketing positioning. For critical business functions, proven suitability and alignment with use cases outweighs novelty or innovation claims.
NEW QUESTION # 28
An organization embeds AI into existing processes without integrating AI risk practices into enterprise governance. Which of the following should a risk practitioner regard as the GREATEST organizational risk?
Answer: D
Explanation:
When AI is deployed without governance integration, no formal structure exists to assign control ownership, coordinate risk management activities, or align AI decision-making with organizational objectives. This structural void produces divergent, fragmented, and potentially conflicting risk management efforts.
Why C is Correct: According to ISACA AAIR, unclear ownership is the greatest organizational risk from AI operating outside governance structures. Without designated owners, controls may be applied inconsistently across business units, different teams may implement conflicting approaches, and no one is responsible for ensuring AI activities align with enterprise objectives. This governance vacuum creates unmanaged risks and organizational incoherence.
Why A is Wrong: Regulatory compliance documentation gaps are significant but are a downstream symptom of poor governance rather than the root organizational risk. Documentation failures can be remediated more easily than fundamental ownership gaps.
Why B is Wrong: Technical-business alignment is an important concern but represents a strategic planning challenge rather than the greatest organizational risk from absent governance. Alignment can be achieved through business case processes without full governance integration.
Why D is Wrong: Executive approval difficulty is an organizational change management challenge. It reflects organizational politics rather than a structural risk from absent governance. Approval processes function independently of AI governance integration.
NEW QUESTION # 29
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