AAISM AI Risk Management Practice Test 6 — 15 Questions

Question 1

An AI risk analyst is assessing a computer vision model used for quality inspection on a manufacturing line. The model occasionally misclassifies defective parts as acceptable, and the defect rate is low. Which risk metric BEST communicates the operational exposure to plant leadership?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

False negative rate for defective parts, expressed as expected escaped defects per shift.. In a low defect-rate inspection process, the dominant risk is defective parts escaping detection. The false negative rate isolates that failure mode, and converting it into expected escaped defects per shift makes the exposure concrete for plant leadership. This is more actionable than aggregate accuracy or threshold-independent metrics.

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Area under the receiver operating characteristic curve (AUC-ROC).

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Overall model accuracy across all inspected parts.

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Model inference latency measured in milliseconds per part.

Question 2

A financial services firm uses an AI model to approve loan applications. The model was trained on historical data that reflects past lending biases. An internal audit finds that the model's decisions disproportionately deny loans to applicants from certain demographic groups, even though those groups are not explicitly represented in the input features. The AI security manager must address this risk. Which action is MOST effective for mitigating the identified bias?

Accepted Answer

Apply a bias mitigation technique such as adversarial debiasing during model training to reduce disparate impact across protected groups.. Bias that persists despite the absence of explicit protected attributes is often embedded in the model's learned representations through proxy variables. Adversarial debiasing actively removes this encoded bias during training by preventing the model from retaining information about protected groups. Simply removing attributes or adding data does not correct the learned bias, and post-processing may introduce other issues.

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Implement a post-processing step that adjusts the model's decision threshold for each demographic group to achieve statistical parity.

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Increase the size of the training dataset by collecting more historical loan data to improve the model's overall accuracy.

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Remove all demographic attributes from the training dataset and retrain the model.

Question 3

A bank's AI governance committee is reviewing a new generative AI tool for credit memo summarization. The vendor provides SOC 2 Type II reports but refuses to disclose training data sources, citing trade secrets. The committee must decide whether to approve the tool for production use. Which action BEST aligns with ISACA's AI risk management principles?

Accepted Answer

Conduct a formal risk assessment documenting the data provenance gap, require contractual attestations, and approve a limited pilot with monitoring before full rollout.. The most defensible action combines formal risk assessment with compensating controls and a bounded pilot. ISACA guidance emphasizes that AI risk cannot always be eliminated but must be identified, evaluated, and managed with documented decisions. Contractual attestations and monitoring create accountability where transparency is limited, and a pilot limits potential harm while generating evidence for a full rollout decision.

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Reject the tool outright because the vendor's refusal to disclose training data sources makes risk assessment impossible.

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Approve the tool with standard data loss prevention controls, since SOC 2 Type II validates the vendor's security posture.

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Escalate the decision to the vendor's account executive to negotiate disclosure of training data sources as a condition of purchase.

Question 4

An AI governance team is mapping controls for a credit-scoring model to the NIST AI Risk Management Framework. The team has completed the MAP function and is now working on MEASURE. Which activity is MOST appropriate for the MEASURE function?

Accepted Answer

Applying fairness metrics and robustness testing to quantify the model's discriminatory and adversarial risk.. The NIST AI RMF MEASURE function is about analyzing and assessing AI risks using metrics, methods, and tools. Applying fairness metrics and robustness testing quantifies discriminatory and adversarial risk, which is a direct MEASURE activity. Documenting context belongs to MAP, prioritizing resources belongs to MANAGE, and defining tolerance is a governance precursor.

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Defining the organization's risk tolerance and appetite for AI systems.

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Establishing a process to prioritize and allocate resources to identified AI risks.

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Documenting the model's intended purpose, stakeholders, and deployment context.

Question 5

A global retailer is deploying a generative AI assistant that summarizes customer service transcripts for human agents. The legal team is concerned about privacy risk from the assistant retaining or regurgitating personally identifiable information (PII) from previous conversations. Which TWO controls BEST reduce this risk? (Choose two.)

Accepted Answer

Add a PII detection and redaction layer on both the input transcripts and the generated summaries.. The privacy risk stems from the assistant retaining or retrieving PII across sessions. Session isolation with no cross-session memory prevents one customer's data from appearing in another's summary, while input and output redaction catches residual identifiers. Together they address both the retrieval path and the generated output, which are the two points where leakage occurs.

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Increase the model's temperature setting to reduce deterministic recall of training data.

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Require human agents to sign an acceptable use policy prohibiting them from viewing PII.

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Apply differential privacy during fine-tuning of the assistant on historical transcripts.

Question 6

A healthcare organization is deploying a generative AI chatbot that provides medical information to patients. The AI security manager is concerned about the risk of the chatbot generating harmful or misleading medical advice. Which control is MOST appropriate to mitigate this risk before deployment?

Accepted Answer

Fine-tune the generative model on a curated dataset of clinically validated medical information and implement a guardrail that restricts responses to that knowledge base.. In healthcare, generative AI must be grounded in validated knowledge to prevent harmful hallucinations. Fine-tuning on curated clinical data combined with a retrieval guardrail that limits responses to that data ensures the chatbot provides accurate, safe information. Content filtering, disclaimers, or full human review are either ineffective, insufficient, or impractical for this use case.

Answer

Require all chatbot responses to be reviewed and approved by a licensed physician before being sent to the patient.

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Implement a content filter that blocks any response containing medical terminology not approved by the organization's clinical team.

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Deploy the chatbot in a limited pilot with a disclaimer that the information is not medical advice and monitor user feedback for harmful outputs.

Question 7

An AI risk manager is preparing a board-level report on a portfolio of machine learning models. The board wants to understand which models pose the greatest potential for financial loss. Which approach BEST aligns model risk assessment with enterprise risk management?

Accepted Answer

Score each model by combining decision impact, failure likelihood, and control effectiveness into a risk rating.. Aligning model risk with enterprise risk management requires expressing exposure in terms of impact, likelihood, and control effectiveness. That structure allows the board to compare model risk against other enterprise risks and to prioritize oversight where potential financial loss is greatest. Inventory and vulnerability counts alone do not convey that exposure.

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List all models with their accuracy metrics and let the board interpret the financial implications.

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Report the total number of models in production and the growth rate of the AI portfolio.

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Rank models by the number of open security vulnerabilities reported by the scanning tool.

Question 8

A hospital is deploying an AI triage system that recommends patient priority levels. The risk committee wants to ensure ongoing risk visibility after go-live. Which TWO activities provide continuous risk visibility for this deployed system? (Choose two.)

Accepted Answer

Monitoring model input distribution drift against the training baseline.. Continuous risk visibility after deployment requires monitoring activities that generate ongoing signals. Input distribution drift monitoring detects when live data diverges from training conditions, and tracking clinician override rates captures real-world disagreement that may indicate model errors or edge cases. Static artifacts such as validation reports, one-time audits, and architecture documentation do not provide continuous visibility.

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Documenting the model architecture and hyperparameters in the model registry.

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Conducting a one-time fairness audit before the system goes live.

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Reviewing the model's original validation accuracy report from the pre-deployment assessment.

Question 9

A financial services firm has deployed a credit-scoring model, but the model owner cannot produce a defensible rationale for the weighting of each input feature, and the model card omits the intended use and known limitations. The CISO wants to align the AI system with ISACA's AI risk management expectations. Which action should be taken FIRST to address this gap?

Accepted Answer

Complete the model card and document feature rationale, intended use, and known limitations as part of the AI system inventory.. The scenario describes a governance and documentation deficiency: the model lacks a defensible feature rationale and omits intended use and known limitations. Completing the model card and documenting these elements restores traceability and accountability, which are foundational AI risk management activities. Other options address technical security testing, model redesign, or data protection, none of which close the documentation gap that prevents the business from understanding and accepting the model's residual risk.

Answer

Commission a full adversarial red-team exercise against the credit-scoring model before any documentation changes are made.

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Immediately retrain the model with a different algorithm that offers inherently higher interpretability.

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Encrypt the training dataset at rest and restrict access to the model's API endpoint.

Question 10

A retail company's AI-powered recommendation engine begins suggesting products that are culturally insensitive in certain regions. The model has not been changed, but sales data from a newly acquired subsidiary was recently added to its training pipeline. Which risk management principle does this scenario BEST illustrate?

Accepted Answer

Model drift caused by changes in the data supply chain can introduce new ethical and reputational risks.. The scenario shows that integrating new data from an acquisition altered the recommendation engine's behavior, producing culturally insensitive outputs without any model code change. This is a data supply chain induced drift that creates ethical and reputational risk, underscoring the need to govern data provenance and monitor model behavior after data pipeline changes.

Answer

Acquiring a subsidiary always requires retraining all enterprise AI models from scratch.

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Recommendation engines are inherently unbiased and only reflect objective customer preferences.

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Ethical risks in AI are only relevant during the initial design phase and not after deployment.

Question 11

An AI security manager is assessing the risk of model inversion attacks on a machine learning model that predicts patient outcomes. The model is accessible via an API that returns confidence scores. Which TWO controls are MOST effective at reducing the risk of model inversion? (Choose two.)

Accepted Answer

Limit the API to return only the top-1 predicted class without confidence scores.. Model inversion attacks exploit the model's tendency to memorize training data, often using confidence scores to reconstruct sensitive information. Limiting API output to top-1 class removes the granular data attackers need, while differential privacy mathematically bounds the influence of any single training record, directly preventing memorization. These two controls together provide strong defense, whereas rate limiting, encryption, and smaller models do not adequately address the core risk.

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Encrypt the model's parameters at rest and in transit.

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Use a model architecture with fewer parameters to reduce its memorization capacity.

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Implement rate limiting on the API to restrict the number of queries per user per hour.

Question 12

An enterprise risk committee is reviewing a generative AI assistant that recommends credit-limit increases. The model is a third-party SaaS large language model (LLM) accessed via API, with no visibility into its training data, weights, or fine-tuning process. The committee wants to know which risk treatment best addresses residual model risk before the assistant goes live, given that the vendor will not permit audits of the model itself.

Accepted Answer

Implement a human-in-the-loop review gate where a credit officer must approve every recommended limit increase before it is applied.. When a model cannot be audited or validated at the source, the enterprise must apply compensating controls at the point of decision. A human-in-the-loop approval gate ensures no unverified model output directly affects a customer, creating an auditable record and preserving accountability. Vendor attestations, randomness tuning, and insurance address adjacent concerns but do not reduce the residual model risk created by an opaque third-party LLM.

Answer

Increase the model's temperature setting so recommendations vary more and bias is averaged out across multiple runs.

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Purchase cyber-liability insurance that explicitly names AI model error as a covered peril and close the risk item.

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Request the vendor's SOC 2 Type II report and rely on it as evidence that model risk has been transferred.

Question 13

A security analyst is reviewing the risk register for a deployed credit-scoring AI model and notices that the 'Model Drift' risk entry has no owner and no monitoring threshold. The model currently performs well on the original validation set. What is the MOST appropriate action for the analyst to take?

Accepted Answer

Assign an accountable risk owner, define drift monitoring thresholds and triggers, and establish a documented response process.. An unowned, threshold-less drift risk in a credit-scoring model is an unmanaged risk, not an accepted one. Effective AI risk management requires an accountable owner, quantified monitoring thresholds with alerting, and a documented response process, which together turn a latent risk into a governed control. Deferring, decommissioning, or passively accepting the risk all fail to establish the governance the scenario demands.

Answer

Immediately decommission the model and replace it with a rules-based scoring system to eliminate drift risk entirely.

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Document the drift risk in the register as accepted, noting that the model's current accuracy exceeds the business requirement.

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Wait until the next scheduled annual model retraining to reassess drift, since the model currently meets performance targets.

Question 14

An AI governance lead is mapping the organization's AI risk register to the NIST AI Risk Management Framework. A business unit asks why the 'Map' function must be completed before the 'Measure' function is fully executed for a new fraud-detection model. Which response BEST justifies the sequence?

Accepted Answer

Mapping identifies the model's context, purpose, and affected stakeholders, which establishes the baseline against which measurement metrics and tolerances are defined.. The Map function establishes context, purpose, dependencies, and affected stakeholders, and that context determines which risks are relevant and what metrics and tolerances are meaningful. Measurement without mapping yields data that cannot be tied to business impact or risk appetite. The framework's structure reflects this dependency, so completing mapping first ensures that subsequent measurement, monitoring, and treatment activities target the risks that actually apply to the model's deployment context.

Answer

Mapping must precede measurement because the Measure function can only be applied to models that have already achieved full production maturity.

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Measurement requires production telemetry, and the Map function is the mechanism that deploys the monitoring agents into the inference pipeline.

Answer

Mapping is a regulatory filing requirement that must be submitted to the supervisory authority before any quantitative testing may begin.

Question 15

A company uses an AI model to screen job applicants. An internal audit finds that the model's decisions cannot be explained in terms a rejected candidate could understand, and the company operates in a jurisdiction with a right-to-explanation law. Which risk management response is MOST appropriate?

Accepted Answer

Implement post-hoc explanation techniques and a governance process that translates model outputs into candidate-understandable reasons, while monitoring explanation fidelity.. A right-to-explanation law requires that rejected candidates receive understandable reasons for decisions. Implementing post-hoc explanation techniques and a governance process to translate model outputs into candidate-friendly reasons addresses this directly while preserving the model. Monitoring explanation fidelity ensures the explanations remain valid as the model evolves, which is essential for sustained legal compliance.

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Remove all demographic data from the training set to eliminate the need for explanations.

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Replace the model with a simpler, inherently interpretable model such as a logistic regression or decision tree, accepting a possible accuracy trade-off.

Answer

Document the model's architecture and training procedure in detail and provide that documentation to rejected candidates upon request.