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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| Topic 2: AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI governance communication and reporting - AI risk monitoring and continuous improvement |
| Topic 3: AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks |
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NEW QUESTION # 154
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
Answer: B
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 # 155
An organization formally accepts residual risk for a low-impact AI capability after confirming it falls within the enterprise risk tolerance.
What is the primary benefit of this risk acceptance action?
Answer: C
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. When residual risk is within tolerance and affects non-critical functions, formal acceptance is proportionate and allows scarce mitigation resources to be directed toward higher-impact exposures.
Acceptance must still be documented and monitored rather than treated as inaction. This makes option A, It enables the allocation of mitigation resources to more critical AI-specific issues, 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 # 156
Which of the following is the BEST governance approach for balancing risk management and operational flexibility across diverse AI applications?
Answer: C
Explanation:
AI governance across diverse applications requires frameworks flexible enough to accommodate varying risk profiles, regulatory environments, and operational contexts while maintaining consistent governance standards. Rigid or overly centralized approaches reduce operational effectiveness.
Why B is Correct: The ISACA AAIR framework advocates for adaptable governance frameworks that can be scaled and tailored to specific AI use cases. A risk-based, adaptable framework applies more rigorous controls to high-risk applications while allowing operational flexibility for lower-risk uses. This balance enables innovation while maintaining appropriate risk oversight-a core principle of proportionate AI governance.
Why A is Wrong: Single-regulation compliance focus creates compliance tunnel vision that may miss material risks not covered by that regulation. AI governance must address the full risk landscape, not just one regulatory framework.
Why C is Wrong: External consultants provide periodic independent assurance, not governance. Relying on external reviews for governance would be episodic rather than continuous, creating governance gaps between review cycles.
Why D is Wrong: Centralized decision-making creates operational bottlenecks and slows AI deployment.
Effective governance delegates decision authority appropriately while maintaining oversight, rather than centralizing all decisions in a single function.
NEW QUESTION # 157
An organization has developed an AI code of conduct outlining ethical use, data privacy, and transparency principles. Which of the following is the BEST approach to integrate the code of conduct into workforce training?
Answer: D
Explanation:
Effective ethics training must be relevant to the specific roles and responsibilities of each workforce segment, and must be reinforced over time as AI applications and ethical challenges evolve. Generic, one-time training produces shallow compliance rather than genuine ethical competence.
Why C is Correct: The ISACA AAIR Study Guide emphasizes role-tailored, continuous education as the best approach for embedding ethical principles into workforce behavior. Different roles-developers, business users, risk practitioners, executives-interact with AI in fundamentally different ways and face different ethical challenges. Tailored content ensures relevance, while scheduled refreshers maintain awareness as the ethical landscape changes with new AI deployments and regulatory developments.
Why A is Wrong: Onboarding incorporation is a starting point but insufficient alone. Ethics are not learned once at hire-they must be continuously reinforced as employees encounter new AI applications and ethical dilemmas in practice.
Why B is Wrong: External providers can deliver quality content but may not understand the organization's specific AI applications, culture, or risk profile. External delivery also tends to be episodic rather than integrated into ongoing role responsibilities.
Why D is Wrong: Focusing on legal compliance creates a rule-following culture rather than genuine ethical judgment. Compliance knowledge is necessary but insufficient for building the ethical reasoning skills needed for novel AI situations not covered by existing regulations.
NEW QUESTION # 158
Which of the following BEST mitigates privacy exposure when using datasets for AI model training that include personal information?
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. De- identification directly reduces privacy exposure by removing or transforming identifying information before personal data is used for training. Authentication and human review protect access or oversight but do not remove personal information from the dataset itself. This makes option C, Implementing robust de- identification controls, 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 # 159
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