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Isaca AAIR Sample Question Answers
Question # 1
Which of the following is the GREATEST risk when an organization relies only onadversarial training to protect a private AI model in a testing environment?
A. Inefficient model training cycles B. Presence of unaddressed system vulnerabilities C. Overfitting to limited datasets D. Increased likelihood of exposing proprietary algorithms
Answer: B
Explanation: Adversarial training improves model robustness against known attack patterns by
incorporating adversarial examples into the training process. However, no single security
technique provides comprehensive protection—adversarial training addresses only the
attack vectors it was designed for, leaving other vulnerabilities unaddressed.
Why B is Correct: The ISACA AAIR security defense-in-depth guidance identifies residual
system vulnerabilities as the greatest risk when adversarial training is the sole security
measure. Adversarial training protects against specific attack types (evasion, perturbation)
but does not address infrastructure vulnerabilities, API security weaknesses, model
inversion attacks, membership inference, or other security risks present in a testing
environment. A defense-in-depth approach is required for comprehensive protection.
Why A is Wrong: Adversarial training does increase computational requirements and may
extend training cycles, but inefficiency is an operational concern rather than a security risk.
The security risk of unprotected vulnerabilities significantly outweighs training cycle
efficiency.
Why C is Wrong: Overfitting to adversarial training examples is a model quality concern
that can be managed through standard regularization techniques. It represents a model
performance trade-off, not the greatest security risk from relying solely on adversarial
training.
Why D is Wrong: Exposure of proprietary algorithms is an intellectual property risk that is
not specifically increased by relying on adversarial training. Algorithm confidentiality is
protected through access controls and encryption, which are separate from the adversarial
training approach.
Question # 2
An organization plans to deploy AI for customer service automation. Which of the followinggovernance approaches BEST aligns culture and risk tolerance?
A. Implement continuous monitoring of AI system key risk indicators (KRIs). B. Emphasize regulatory compliance concerns when vetting AI business cases. C. Scale controls to AI system impact and involve business stakeholders to obtain buy-in. D. Focus enterprise AI risk management on projects with the greatest long-term impact.
Answer: C Explanation: Within the ISACA Advanced in AI Risk framework, governance decisions should align AI
use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal
or ethical obligations. Governance should scale controls to the impact of the customerservice AI system and involve business stakeholders so the control environment reflects
risk tolerance and gains operational support. Monitoring alone does not create cultural
alignment. This makes option C, Scale controls to AI system impact and involve business
stakeholders to obtain buy-in, 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.
Question # 3
Which of the following BEST helps to ensure an AI system's potential human rights harmsare managed?
A. Classifying personal information according to sensitivity and criticality B. Aligning AI policies to global privacy and security frameworks C. Engaging stakeholders in recurring risk treatment processes D. Monitoring system output metrics periodically
Answer: C
Explanation: Within the ISACA Advanced in AI Risk framework, governance decisions should align AI
use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal
or ethical obligations. Potential human-rights harms are best managed through recurring
stakeholder engagement in risk treatment because affected people may reveal impacts that
technical metrics or internal policy mapping miss. Ongoing participation supports
identification, escalation, and remediation of evolving harms. This makes option C,
Engaging stakeholders in recurring risk treatment processes, 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.
Question # 4
An organization has deployed an AI system to automate critical data analysis functions.
Which of the following is the MOST appropriate way for the risk practitioner to assess the multiple sources of risk associated with this situation?
A. Prioritize the risk factors most likely to generate substantial harm. B. Quantify the financial impact of competitors' realized risk events on AI initiatives. C. Rate each risk factor independently as a basis for ordering mitigation actions. D. Document the exploitable technical limitations of all AI system components
Answer: A
Explanation: When multiple risk sources are present in a critical AI deployment, the risk practitioner must
apply a prioritization framework that focuses resources on the risks with the greatest
potential for organizational harm. This risk-based prioritization is more effective than
comprehensive but undifferentiated risk cataloging.
Why A is Correct: The ISACA AAIR risk assessment methodology prioritizes risk factors
based on potential harm severity as the most appropriate approach for critical AI systems.
Focusing on risks most likely to generate substantial harm ensures that the organization's
risk management resources are directed toward the exposures that matter
most—protecting the critical functions that the AI system supports and preventing the most
consequential adverse outcomes.
Why B is Wrong: Quantifying competitors' risk events provides external benchmarking data
but cannot accurately characterize the organization's specific risk profile. Competitor risk
events may involve different AI architectures, use cases, and organizational contexts that
make direct comparison unreliable.
Why C is Wrong: Rating each risk factor independently without integration produces a
fragmented view that misses risk correlations, cascade effects, and the compounding
nature of multiple simultaneous risk factors. Independent ratings also do not inherently lead
to the harm-based prioritization needed for critical systems.
Why D is Wrong: Documenting technical limitations is a useful input to risk identification but
represents a technical inventory activity rather than a comprehensive risk assessment
methodology. Technical limitations are one category of risk factor among
many—operational, governance, data quality, and third-party risks also require
assessment.
Question # 5
Which of the following is the PRIMARY purpose of maintaining comprehensive model cardsand documentation?
A. Justifying model use cases B. Preserving audit trails C. Listing technical specifications D. Providing model transparency
Answer: D Explanation: Model cards are standardized documents that communicate key information about AI
models, including their intended use, training data, performance characteristics, limitations,
and ethical considerations. They serve as a primary transparency instrument in AI
governance.
Why D is Correct: According to the ISACA AAIR curriculum, the primary purpose of model
cards is to provide transparency to stakeholders—including developers, users, auditors,
and regulators. Transparency enables informed decision-making about model deployment,
helps identify potential misuse, and supports responsible AI governance across the life
cycle.
Why A is Wrong: Justifying use cases is a secondary benefit. Model cards are not primarily
advocacy documents; their core function is objective disclosure of model characteristics
and limitations.
Why B is Wrong: Preserving audit trails is a governance function served by version control
and change management systems. While model cards contribute to audit readiness, it is
not their primary purpose.
Why C is Wrong: Technical specifications represent only a subset of model card content.
Model cards go beyond technical detail to address fairness, bias, intended use boundaries,
and societal impact considerations
Question # 6
Which of the following is the PRIMARY risk reduction benefit of embedding secure codingpractices throughout the AI development life cycle?
A. More rapid detection of model drift during pre-deployment testing B. Fewer model vulnerabilities that can be exploited by malicious actors C. Reduced need for manual review of model outputs and decisions D. Increased predictive accuracy in production environments
Answer: B 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. Secure coding throughout the AI development lifecycle reduces
exploitable software and integration vulnerabilities before deployment. It does not directly
detect model drift, eliminate human review, or guarantee predictive accuracy. This makes
option B, Fewer model vulnerabilities that can be exploited by malicious actors, the
strongest answer. The other choices describe narrower technical, operational,
performance, or administrative considerations and do not address the primary riskmanagement 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.
Question # 7
Which of the following is the PRIMARY reason to lower AI model temperature?
A. To mitigate the risk of persistent bias in responses to users B. To enhance consistency and accuracy of model outputs C. To diversify ideas and recommendations generated by the model D. To reduce energy consumption and environmental impact
Answer: B
Explanation: Temperature is a hyperparameter in language model generation that controls output
randomness. Lower temperatures make the model more deterministic—concentrating
probability mass on the most likely tokens and producing more consistent, predictable
outputs. Higher temperatures introduce more randomness and diversity.
Why B is Correct: According to ISACA AAIR model configuration guidance, lowering model
temperature is primarily used to enhance production applications requiring reliable, reproducible responses—such as customer
service, compliance reporting, or technical documentation—lower temperature ensures the
model consistently generates the most appropriate response based on its learned
knowledge, reducing variability and improving output quality.
Why A is Wrong: Temperature adjustment does not directly mitigate bias. Bias in AI models
is a function of training data and model architecture, not output randomness. A biased
model at low temperature will consistently generate biased outputs; lowering temperature
may actually make bias more persistent by reducing variation.
Why C is Wrong: Diversifying ideas and recommendations is achieved by increasing
temperature, not lowering it. Higher temperature is used for creative tasks where variety is
valuable; lower temperature is used for tasks requiring precision and consistency.
Why D is Wrong: Model temperature has no direct relationship to computational energy
consumption. Energy use is primarily driven by model size, computation requirements, and
inference frequency—not the temperature parameter. consistency and accuracy of outputs. In
Question # 8
Which of the following is the MOST important reason for a risk practitioner to classify AI riskusing threat actor profiles?
A. To align AI threat and vulnerability risk with the overall IT control taxonomy B. To tailor controls to adversary motivations and capabilities C. To develop response metrics for AI cybersecurity incidents D. To ensure external threats to corporate assets are given highest priority
Answer: B
Explanation: Threat actor profiling characterizes the motivations, capabilities, and likely attack methods
of potential adversaries. In AI risk management, understanding who the likely attackers are and what they seek enables the design of controls specifically matched to the actual threat
landscape.
Why B is Correct: According to ISACA AAIR threat-based risk management guidance, the
most important reason for threat actor profiling is to tailor controls to adversary motivations
and capabilities. Different threat actors—nation-state attackers, criminal organizations,
competitors, insiders, activists—have different objectives (espionage vs. financial gain vs.
disruption), capabilities (sophisticated vs. opportunistic), and methods. Controls calibrated
to actual threat actor profiles are significantly more effective than generic controls that may
not address the specific threats the organization actually faces.
Why A is Wrong: Aligning AI threats with IT control taxonomy is a governance integration
activity that improves control consistency but does not capture the threat actor-specific
tailoring value of profiling. Taxonomy alignment is an administrative benefit; threat-tailored
controls are a security effectiveness benefit.
Why C is Wrong: Response metrics for cybersecurity incidents are developed for incident
management planning. Threat actor profiling informs control design and incident response
strategies but is not primarily used to develop response metrics.
Why D is Wrong: Prioritizing external threats over internal threats is a security strategy
choice that threat actor profiling does not prescribe. Many AI attacks, including insider
threats and social engineering, are internal. Profiling should result in appropriate
prioritization based on actual threat likelihood, not a blanket prioritization of external
threats.
Question # 9
A risk practitioner assesses a new AI system and determines that the risk is within theorganization's risk tolerance. Which of the following is the BEST recommendation to ensuresystem controls remain effective over time?
A. Alignment with recognized AI control frameworks B. Ongoing AI security and risk awareness training C. Continuous monitoring for data and performance drift D. Periodic regulatory compliance reviews
Answer: C
Explanation: Even when an AI system is initially assessed as within risk tolerance, its risk profile evolves
as the system encounters new data, the operational environment changes, and model
performance drifts. Controls that were effective at deployment may become insufficient as
these changes accumulate.
Why C is Correct: The ISACA AAIR operational monitoring guidance identifies continuous
monitoring for data and performance drift as the most important mechanism for maintaining
control effectiveness over time. Drift detection provides early warning when the AI system
begins behaving differently from its validated state—enabling timely control adjustments
before risk tolerance is breached. This is particularly critical because AI systems can
degrade gradually in ways not visible without active monitoring.
Why A is Wrong: Framework alignment establishes the control baseline but does not
actively verify that controls remain effective as the system evolves. Frameworks provide
structure; monitoring provides assurance.
Why B is Wrong: Security and risk awareness training is an important human capability
development activity but does not detect technical changes in AI system behavior. Training
does not substitute for technical monitoring.
Why D is Wrong: Periodic compliance reviews occur at scheduled intervals and may miss
drift that develops between review cycles. Continuous monitoring provides real-time
detection that periodic reviews cannot match.
Question # 10
Which of the following is the BEST course of action to mitigate risk during model selectionof supervised or unsupervised algorithms?
A. Emphasize the generalization capability of algorithms. B. Require the use of supervised learning for model training projects. C. Prioritize cost reductions related to computational requirements. D. Align algorithmic capabilities to intended use cases.
Answer: D
Explanation: Algorithm selection is a foundational risk management decision in AI development. The
wrong algorithm for a given use case can produce inaccurate, unreliable, or harmful
outputs regardless of the quality of training data or computational resources applied.
Why D is Correct: The ISACA AAIR model development guidance identifies use case
alignment as the most critical algorithm selection criterion. Supervised and unsupervised
learning are suited to fundamentally different problem types—supervised learning requires
labeled training data and learns mappings to known outputs; unsupervised learning
discovers patterns in unlabeled data. Selecting algorithms whose capabilities match the
use case's structure and objectives prevents systematic performance failures and
misapplied AI.
Why A is Wrong: Generalization capability is an important model quality criterion but
represents one of many algorithmic properties. Strong generalization on the wrong problem
type still produces poor results. Use case alignment precedes generalization as a selection
criterion.
Why B is Wrong: Requiring supervised learning for all training projects is an inappropriate
blanket policy. Many valuable use cases—anomaly detection, customer segmentation,
exploratory analytics—are better served by unsupervised approaches. Mandating
supervised learning prevents optimal use case matching.
Why C is Wrong: Computational cost is a resource management consideration. Optimizing
for cost at the expense of use case fit risks deploying inappropriate models that produce
unreliable outputs, creating far greater costs through remediation or harm.
