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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - AI governance communication and reporting |
| Topic 2: AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI model and data risk identification - AI development, deployment, and monitoring risks | |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment |
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NEW QUESTION # 41
Risk practitioners use automated tools to generate potential AI risk scenarios. Which of the following represents the GREATEST risk from that approach?
Answer: D
Explanation:
Automated risk scenario generation tools operate based on programmed logic, historical data, and pattern recognition. They may excel at generating scenarios based on known risks and documented processes but struggle to account for complex organizational interdependencies that are not fully captured in their data inputs.
Why D is Correct: The ISACA AAIR risk scenario development guidance identifies the failure to account for process interdependencies as the greatest risk from automated scenario generation. AI systems do not operate in isolation-they are embedded in complex organizational ecosystems where failures cascade through interconnected processes, systems, and stakeholders. Automated tools may miss these interdependencies, producing scenarios that are technically accurate in isolation but miss the most consequential cascade effects.
Why A is Wrong: Complexity in likelihood and impact scoring is a risk quantification challenge that affects scenario prioritization but does not result in missing scenarios entirely. Complex scoring can be managed through additional analytical methods.
Why B is Wrong: Emerging adversarial attack vectors are a potential blind spot for any tool or analyst working from historical data, but this is a known limitation of retrospective approaches that can be supplemented with threat intelligence. It does not represent the distinctive risk of automated scenario generation.
Why C is Wrong: Underestimating model change impacts is a scenario calibration issue that represents a less severe risk than missing entire categories of scenarios arising from unmodeled interdependencies.
NEW QUESTION # 42
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: B
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 # 43
Which of the following should be the MOST important area of focus during the development of data security risk scenarios specific to AI?
Answer: B
Explanation:
AI systems introduce unique security threat vectors that differ fundamentally from conventional IT security scenarios. Risk scenarios must address AI-specific attacks-model poisoning, adversarial inputs, output manipulation-that conventional security frameworks do not cover.
Why A is Correct: The ISACA AAIR AI security risk scenario guidance focuses on attacks that specifically exploit AI system properties-particularly techniques that maliciously alter AI outputs. These AI-specific attack vectors (adversarial examples, model inversion, prompt injection, output manipulation) represent the most important focus for AI security risk scenario development because they target capabilities unique to AI systems and cannot be addressed by repurposing conventional IT security scenarios.
Why B is Wrong: Business unit readiness documentation is a change management and organizational capability assessment activity. It supports AI adoption planning but does not constitute AI security risk scenario development.
Why C is Wrong: Access policy development is an important security control activity but represents control design rather than risk scenario development. Access policies respond to identified risks; they are not themselves risk scenarios.
Why D is Wrong: Quantum encryption is an emerging cryptographic technology addressing future threats to classical encryption. While relevant for long-term data protection planning, it represents a specialized and forward-looking concern rather than the most important focus for current AI security risk scenarios.
NEW QUESTION # 44
An organization seeks to implement a new AI system that uses customer information to create targeted product recommendations. Which of the following is the MOST important consideration to ensure the system complies with regulatory requirements?
Answer: B
Explanation:
Privacy and data protection regulations worldwide-including GDPR, CCPA, and sector-specific laws- impose strict requirements on the collection, use, and processing of personal information. Customer data used for AI systems must be obtained through lawful means with appropriate consent for the specific processing purpose.
Why A is Correct: According to ISACA AAIR guidance on regulatory compliance, the legal basis for processing personal data is the foundational requirement. An AI system built on data collected without proper consent or legal authorization exposes the organization to regulatory penalties, reputational damage, and forced shutdown of the system. Consent must be specific to the AI use case, not merely generic data collection consent.
Why B is Wrong: Backup and storage protocols address data security and resilience, which are compliance requirements but secondary to the lawfulness of data collection. Securely storing improperly obtained data does not cure the regulatory violation.
Why C is Wrong: Human review of recommendations is a governance safeguard for accuracy and fairness, not a regulatory compliance requirement for data collection. Many regulations do not require human review of recommendation systems.
Why D is Wrong: Supervised learning is a modeling technique that does not address regulatory compliance regarding data sourcing. The training methodology is irrelevant to whether the underlying data was legally obtained.
NEW QUESTION # 45
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Answer: C
Explanation:
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.
NEW QUESTION # 46
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