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| Section | Objectives |
|---|---|
| AI Risk Management | - Risk identification and assessment for AI systems
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| Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| AI Lifecycle Controls | - Controls across AI development lifecycle
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| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
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83. Frage
To reinforce organization-wide ethical norms and risk recognition, which of the following is MOST important to integrate into AI user training?
Antwort: B
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.
84. Frage
Which of the following would be of GREATEST concern to a risk practitioner reviewing the testing and validation of an AI-driven technical support system?
Antwort: B
Begründung:
AI-driven technical support systems rely on accurate, current knowledge to resolve user issues. Model drift causes the system to diverge from real-world conditions, producing inaccurate outputs that erode user trust, increase escalations, and potentially cause harm if incorrect technical guidance is followed.
Why A is Correct: According to ISACA AAIR validation guidance, inaccurate outputs from model drift represent the greatest risk in a technical support AI because they directly compromise the system's core function-providing correct technical guidance. Inaccurate outputs lead to unresolved issues, potential system damage from wrong instructions, and reputational harm. Unlike the other options, drift-driven inaccuracy affects every user interaction and cannot be remediated without model updates.
Why B is Correct Context: Infrequent training dataset updates are a contributing cause of model drift and are a serious concern, but they are an input factor rather than the manifest risk itself. The concern is the resulting inaccuracy.
Why C is Wrong: Encryption is a security control for data in storage and transit. While important for confidentiality, it does not affect the accuracy of AI outputs or the system's ability to provide correct technical guidance.
Why D is Wrong: Excessive manual sampling is a testing methodology concern that may reduce testing coverage efficiency. However, it represents a process inefficiency rather than a direct risk to output quality- the model's accuracy is the greater concern.
85. Frage
Which of the following is the BEST justification for selecting a risk avoidance strategy when considering whether to deploy a high-impact AI system?
Antwort: B
Begründung:
Risk avoidance is the risk treatment strategy of not engaging in an activity because the risks it presents cannot be adequately mitigated to within acceptable tolerance. For high-impact AI systems, the justification for avoidance must be proportionate to the gravity of the decision to forgo deployment entirely.
Why A is Correct: The ISACA AAIR risk treatment framework identifies potential harm to stakeholders as the most compelling justification for risk avoidance in AI deployment decisions. When a high-impact AI system poses risks of significant harm to individuals, communities, or society that cannot be adequately controlled, avoiding deployment is the ethically and legally appropriate choice. Stakeholder harm-especially irreversible or widespread harm-represents the highest severity risk outcome and justifies the most conservative risk treatment.
Why B is Wrong: Cost reduction objectives are business case considerations, not risk management justifications. Avoiding deployment to reduce costs is a financial decision, not a risk avoidance strategy. Risk avoidance decisions are driven by harm potential, not cost efficiency.
Why C is Wrong: Staff expertise shortages represent an organizational capability constraint that can be addressed through hiring, training, or managed services. A capability gap is a surmountable operational challenge, not a justification for permanently avoiding a valuable deployment.
Why D is Wrong: Data poisoning attack likelihood is a security risk that can be mitigated through appropriate controls-data integrity verification, provenance tracking, anomaly detection. A manageable risk with available mitigations does not justify full risk avoidance when stakeholder harm is not at stake.
86. Frage
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Antwort: C
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
An organization integrates multiple AI services using APIs to enhance a customer support chatbot. Which of the following is the GREATEST risk?
Antwort: B
Begründung:
API integration with external AI services creates data transmission pathways between the organization and external systems. Customer support contexts involve sensitive personal data-account information, contact details, inquiry content-that may be transmitted through these API connections.
Why B is Correct: The ISACA AAIR security and privacy guidance identifies unauthorized disclosure of sensitive data through insecure API connections as the greatest risk in multi-service AI integration. APIs can be vulnerable to interception, inadequate authentication, or misconfiguration. In a customer support context, exposure of personal data via API vulnerabilities creates privacy violations, regulatory liability, and reputational harm-all more severe than the other listed concerns.
Why A is Wrong: Bias and inaccuracy in chatbot responses are real quality risks but represent service quality issues rather than security or privacy breaches. Inaccurate responses are visible and correctable; data breaches may go undetected.
Why C is Wrong: Customer dissatisfaction from operational delays is a service quality and business risk. It is a manageable consequence of performance issues rather than the greatest risk from API-based AI integration.
Why D is Wrong: Insufficient training datasets affect model quality but are a development concern addressed during the model selection phase. They do not represent the primary operational risk of deploying multi- service API integrations in production.
88. Frage
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AAIR Fragen Antworten: https://www.pruefungfrage.de/AAIR-dumps-deutsch.html