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Which of the following is the BEST justification for selecting a risk avoidance strategy when considering whether to deploy a high-impact AI system?
Answer : A
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.
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
Answer : C
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.
Which of the following is the PRIMARY purpose of maintaining comprehensive model cards and documentation?
Answer : D
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.
An organization plans to deploy an AI system that ingests multiple sources with varying completeness and accuracy. Which of the following is the risk practitioner's BEST recommendation?
Answer : C
Data quality directly determines AI model accuracy and reliability. When input sources vary in completeness and accuracy, the AI system is exposed to continuous data quality risks that can produce unreliable outputs. This requires ongoing, real-time quality management rather than periodic or reactive responses.
Why C is Correct: According to ISACA AAIR data quality guidance, implementing continuous real-time QA processes is the most effective approach for managing variable-quality multi-source inputs. Real-time QA identifies and addresses quality issues as data enters the system---before they contaminate model inputs and outputs. This prevents quality problems from accumulating and ensures the model consistently receives the highest-quality available data.
Why A is Wrong: Synthetic data augmentation is useful for addressing data scarcity but does not resolve accuracy and completeness issues in existing real-world sources. Generating synthetic data alongside poor-quality real data does not improve the real data.
Why B is Wrong: Post-implementation assessments are reactive---they identify problems after they have already affected model behavior and potentially produced harmful outputs. Prevention through real-time QA is superior to post-hoc remediation.
Why D is Wrong: Fine-tuning model parameters can improve robustness to input variation but does not address underlying data quality problems. Models trained to tolerate poor data may produce less reliable outputs than models receiving consistently high-quality data.
Which of the following is the MOST important consideration when managing changes to an AI model in production?
Answer : C
Changes to production AI models---including retraining, parameter updates, and architecture modifications---can alter model behavior in ways that introduce new biases, reduce accuracy, or create regulatory compliance issues. Validation before deploying changes is the most critical safeguard.
Why C is Correct: According to ISACA AAIR change management guidance for AI systems, rigorous validation to assess changes' effects on predictive accuracy and model bias is the most important change management activity. Production AI models make real-world decisions affecting people and business outcomes. Unvalidated changes may degrade performance, introduce discriminatory patterns, or create regulatory violations that are difficult to detect and remediate after deployment.
Why A is Wrong: Allowing operational teams to adjust configuration parameters in real time bypasses change control processes and creates untracked, unvalidated changes to model behavior. This represents a governance risk, not an acceptable change management practice.
Why B is Wrong: Access controls for new model functionalities are a security and authorization concern. While important for access governance, they do not address the technical risk that model changes may degrade performance or introduce bias.
Why D is Wrong: Expediting production rollouts to minimize downtime prioritizes availability over quality assurance. Rushing changes without adequate validation trades one operational risk (downtime) for a potentially more severe risk (biased or inaccurate outputs affecting critical decisions).
