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| Section | Objectives |
|---|---|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
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82. Frage
To reinforce organization-wide ethical norms and risk recognition, which of the following is MOST important to integrate into AI user training?
Antwort: A
Begründung:
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.
83. Frage
Which risk treatment is MOST appropriate when an organization's AI system presents residual risk within tolerance and impacts non-critical functions?
Antwort: D
Begründung:
Risk treatment decisions are driven by two factors: whether the residual risk falls within or outside tolerance, and the criticality of the affected function. When both conditions-risk within tolerance AND non-critical function impact-are met, formal risk acceptance is the appropriate and proportionate treatment.
Why A is Correct: According to ISACA AAIR risk treatment guidance, documented formal risk acceptance is the appropriate response when residual risk is within defined tolerance for non-critical functions. Risk acceptance acknowledges the identified exposure, documents the organization's conscious decision to accept it, and establishes accountability for that decision. This proportionate response avoids over-investing in controls for risk that the organization has determined is acceptable.
Why B is Wrong: Recommending increases to tolerance thresholds is a governance manipulation rather than a risk treatment. Adjusting thresholds upward to accommodate risk does not address the risk; it merely reclassifies it as acceptable. This approach undermines risk governance integrity.
Why C is Wrong: Enhancing monitoring to detect deviations represents additional control investment that may be disproportionate for risk that is already within tolerance affecting non-critical functions. Enhanced monitoring is more appropriate when risk is near the tolerance boundary or when trends indicate potential future breach.
Why D is Wrong: Periodic vulnerability scanning is a security assurance activity that identifies technical weaknesses. It represents an ongoing control measure rather than the appropriate risk treatment decision for a residual risk that is already within tolerance.
84. Frage
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?
Antwort: B
85. Frage
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?
Antwort: D
Begründung:
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.
86. Frage
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Antwort: B
Begründung:
Evasion attacks involve adversaries crafting inputs specifically designed to fool AI models into producing incorrect outputs-for example, manipulating images to evade object detection or modifying text to bypass content classifiers. Detecting these attacks requires identifying inputs that are statistically unusual or inconsistent with legitimate use patterns.
Why B is Correct: The ISACA AAIR adversarial AI security guidance identifies anomaly detection as the most effective mitigation for evasion attacks. Anomaly detection systems monitor input distributions, model query patterns, and output characteristics for statistical deviations that indicate adversarial manipulation. By identifying inputs that fall outside expected distributions or trigger unusual model responses, anomaly detection catches evasion attempts before they produce harmful outputs.
Why A is Wrong: API rate limiting controls query frequency to prevent brute-force model probing but does not detect or prevent crafted adversarial inputs sent at normal rates. An attacker can evade rate limits by spacing requests or distributing queries.
Why C is Wrong: Predictive analytics uses historical patterns to forecast future outcomes. It does not specifically detect real-time adversarial manipulation of model inputs.
Why D is Wrong: Feature importance weighting adjusts how much different input features influence model predictions. While it can improve robustness to irrelevant features, it does not detect adversarial inputs specifically crafted to exploit important features.
87. Frage
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