AAISM AI Risk Management Practice Test 7 — 15 Questions

Question 1

An organization is developing an AI risk register as part of its AI governance program. The AI security manager needs to ensure that risks are tracked and managed throughout the AI system lifecycle. Which element is MOST critical to include in the risk register for each identified AI risk?

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

Accepted Answer

The risk owner, likelihood, impact, and current mitigation status.. An AI risk register must contain actionable information for managing each risk. The risk owner ensures accountability, while likelihood and impact allow prioritization. Mitigation status tracks progress in reducing the risk. These elements are essential for any risk management framework, including those aligned with NIST AI RMF or ISO/IEC 23894. Technical details like algorithm or parameter count are secondary.

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The name of the data scientist who developed the model.

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The number of parameters in the model.

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The specific machine learning algorithm used, such as random forest or neural network.

Question 2

A hospital's AI governance lead is classifying an AI triage tool that prioritizes emergency department patients. The tool was trained on five years of historical admission data. A risk assessment must determine the inherent risk tier before any controls are applied. Which factor most directly drives the inherent risk classification of this system?

Accepted Answer

The potential for direct physical harm to patients if the model prioritizes incorrectly.. Inherent risk classification is driven by the severity of consequences to affected parties before any controls are applied. A triage model that can misorder critically ill patients creates potential for direct physical harm, which places it in the highest tier. Dataset size, model architecture, and hosting region are relevant to control design and model quality but do not determine the inherent risk tier.

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Whether the model uses a transformer or gradient-boosted tree architecture.

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The cloud region where the model's inference endpoints are hosted.

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The volume of training records used to build the model.

Question 3

A hospital's AI governance committee is evaluating a vendor-provided clinical decision support model. The vendor refuses to disclose training data composition or model architecture but provides accuracy metrics and a third-party audit attestation. The committee must decide how to treat the residual risk of unknown model provenance. Which approach BEST aligns with AI risk management principles?

Accepted Answer

Define compensating controls such as contractual audit rights, independent validation on local data, and human-in-the-loop review, then document and accept the residual risk.. When a vendor withholds training data and architecture details, the committee cannot eliminate provenance risk, but it can bound it with compensating controls: contractual audit rights, independent validation on local patient data, and human-in-the-loop clinical review. Documenting and accepting the residual risk with an accountable owner makes the decision defensible. Rejection, blind acceptance, and liability shifting each fail to establish the layered assurance the scenario requires.

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Reject the vendor outright, since undisclosed training data composition makes any risk assessment impossible.

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Accept the vendor's accuracy metrics as sufficient evidence and deploy the model without additional controls.

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Require the vendor to sign a liability waiver transferring all clinical decision responsibility to the hospital.

Question 4

A retail company's AI team wants to deploy a customer-facing chatbot powered by a large language model. The chief information security officer asks the AI risk manager to identify the MOST significant risk introduced by this deployment that does not exist with traditional rule-based chatbots.

Accepted Answer

The potential for the model to generate unpredictable, contextually inappropriate, or harmful responses that bypass predefined business rules.. The defining new risk of generative AI in customer-facing applications is non-deterministic output generation. Rule-based chatbots follow predefined paths, so their responses can be fully anticipated and controlled. Large language models generate responses probabilistically, creating the possibility of harmful, off-brand, or policy-violating outputs. This requires a new layer of controls including output moderation, prompt injection defenses, and escalation procedures.

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Increased infrastructure cost due to the computational requirements of large language models.

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The difficulty of integrating the chatbot with existing customer relationship management systems.

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The need to comply with data residency requirements when the model is hosted in a cloud environment.

Question 5

A hospital network is deploying a clinical decision support model that recommends triage priorities. The risk committee wants to establish human oversight that satisfies AI risk management expectations while preserving clinical workflow efficiency. Which TWO design choices BEST achieve meaningful human oversight? (Choose two.)

Accepted Answer

Require the clinician to record an override reason whenever the recommendation is rejected, and route that record into periodic model review.. Effective human oversight requires that the reviewer has enough information to evaluate the recommendation and that their disagreement produces a usable signal. Presenting rationale and uncertainty supports informed judgment, while capturing override reasons converts clinical disagreement into evidence that feeds model review. Together these choices create an oversight control that is both substantive at the point of care and useful for ongoing risk management, rather than a procedural formality.

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Restrict clinician access to the model's inputs so that staff cannot second-guess the recommendation with incomplete information.

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Configure the model to auto-populate the triage decision and require the clinician to click a single confirmation button to accept it.

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Limit oversight to a monthly aggregate dashboard review by the quality committee after decisions have already been executed.

Question 6

A financial services firm has deployed an AI model that flags suspicious transactions. After six months, monitoring shows the model's false-positive rate has drifted from 4% to 11%, generating excessive analyst workload. The data science team confirms the model has not been retrained and the feature pipeline is unchanged. Which risk management action should the AI governance lead prioritize?

Accepted Answer

Investigate the cause of drift by comparing current input data distributions against the training baseline, then decide on remediation.. Performance drift without retraining or pipeline changes points to a shift in data distributions or in the input-to-outcome relationship. The governance lead should first diagnose whether the cause is data drift or concept drift by comparing current inputs to the training baseline. That diagnosis determines whether retraining, recalibration, or upstream data remediation is appropriate. Retraining, threshold tuning, and deferral all act without understanding the cause.

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Document the drift in the risk register and schedule a review at the next quarterly governance meeting.

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Immediately retrain the model on the most recent transaction data and redeploy.

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Raise the model's decision threshold so fewer transactions are flagged and analyst workload returns to normal.

Question 7

A financial services firm is building an AI risk taxonomy for its enterprise risk management framework. The CISO asks which characteristics distinguish AI-specific risks from traditional IT risks so that appropriate controls can be designed. Which TWO characteristics should be included? (Choose two.)

Accepted Answer

AI risk exposure changes continuously as models learn from or are exposed to new data, requiring ongoing rather than point-in-time assessment.. Two characteristics genuinely distinguish AI risk from traditional IT risk: emergent behavior that defies component-level prediction, and continuous change that invalidates point-in-time assessments. Together they justify ongoing monitoring, staged deployment, red-teaming, and version governance. The other characteristics either describe similarities, restate legal misconceptions, or falsely claim determinism, none of which belong in a defensible AI risk taxonomy.

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AI systems are exempt from data protection regulations because their outputs are statistical rather than factual.

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AI models are subject to the same patch management and vulnerability disclosure cycles as conventional enterprise software.

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AI models always produce deterministic outputs, allowing identical risk treatment to traditional deterministic business logic.

Question 8

A multinational manufacturer uses an AI system to optimize supply chain logistics across 12 countries. The AI risk manager is building a third-party risk assessment process for the AI vendor that provides the optimization engine. The vendor uses a proprietary model hosted in a jurisdiction with different data protection laws than the manufacturer's headquarters. Which factor is MOST critical to include in the vendor risk assessment?

Accepted Answer

The vendor's data handling practices, model transparency provisions, and compliance with data protection laws in each jurisdiction where the manufacturer operates.. For a multi-jurisdiction AI deployment, the most critical vendor risk factor is how the vendor handles data across borders and whether it can comply with the data protection laws of every country where the manufacturer operates. Model transparency provisions ensure the manufacturer can meet its own governance obligations. Together these determine the manufacturer's legal exposure and ability to maintain the AI system in production.

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The vendor's use of open-source components in the AI model and associated licensing obligations.

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The vendor's software development lifecycle maturity rating from a recognized industry benchmark.

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The vendor's financial stability and ability to provide long-term support for the optimization engine.

Question 9

A retailer's AI system uses a third-party foundation model accessed through a vendor API to summarize customer support transcripts. During risk assessment, the team realizes the vendor may use submitted content to improve its own models, and the retailer cannot inspect the vendor's training data or security controls. Which approach BEST manages this third-party AI risk?

Accepted Answer

Negotiate contractual terms that prohibit training on submitted data, require breach notification and deletion rights, and then validate those commitments through vendor assessment.. When an organization cannot inspect a vendor's data handling or security controls, the practical treatment is to bind the vendor through contractual obligations and verify those obligations through assessment. Prohibiting training on submitted content, requiring breach notification and deletion rights, and validating the commitments provides enforceable protection and creates evidence for the risk register. Relying on certifications alone, assuming the data is harmless, or hastily self-hosting without capability does not address the identified exposure.

Answer

Accept the risk because the transcripts contain no regulated personal data and the model output is only used internally.

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Rely on the vendor's published security certifications as sufficient evidence that the data handling risk is acceptable.

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Move the workload to a self-hosted open-weight model immediately, regardless of whether the organization has the operational capacity to run it.

Question 10

A global insurer uses an AI model to flag potentially fraudulent claims. Six months after deployment, claim volume patterns shift substantially because of a regional economic downturn, and the model's false-positive rate rises sharply. The risk team must respond in a way that reflects sound AI risk management. Which action is MOST appropriate?

Accepted Answer

Trigger the model's drift response plan: reassess performance by segment, adjust thresholds or retrain, and strengthen human review for affected claims.. The scenario describes concept or data drift caused by an external economic shift, evidenced by a sharp increase in false positives. Sound AI risk management calls for activating the pre-defined drift response: reassess performance by segment, adjust thresholds or retrain, and increase human review for affected claims while remediation proceeds. This protects claimants, restores model performance, and keeps the response within a governed, documented process rather than an improvised or deferred one.

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Retire the model permanently and revert to fully manual claim review until a replacement is validated.

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Suppress reporting of the increased false-positive rate until the next scheduled annual model validation to avoid disrupting operations.

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Leave the model unchanged and rely on the existing appeals process to correct any erroneous fraud flags after claimants complain.

Question 11

An e-commerce company discovers that its recommendation model, trained on historical customer data, systematically under-recommends products to users from certain demographic groups, reducing their access to promotional offers. The legal team flags potential fair-lending-style exposure even though no credit is involved. Which action BEST addresses this risk within an AI risk management framework?

Accepted Answer

Conduct a fairness assessment across demographic groups, document the disparity and its potential harms, then apply mitigation such as reweighting or post-processing and monitor outcomes continuously.. Addressing algorithmic disparity requires measuring outcomes across groups, documenting the disparity and its harms, applying targeted mitigation, and monitoring continuously. This mirrors the standard risk treatment cycle and produces auditable evidence of due diligence. Attribute removal fails because proxies persist, shutdown is disproportionate and unverifiable, and disclaimers change nothing about the outcome, so none of those options genuinely treat the risk.

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Add a disclaimer to the website stating that recommendations are automated and may vary, transferring responsibility to the user.

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Remove all demographic attributes from the training data and retrain, assuming that will eliminate the disparate impact.

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Disable the recommendation model for all users and revert to a static, non-personalized product listing until the issue is resolved.

Question 12

An AI risk manager is presenting to the board on the organization's AI risk appetite. The board asks how the organization should handle an AI system that delivers high business value but has a residual risk level that exceeds the stated risk appetite for model accuracy in financial forecasting. Which response BEST reflects ISACA's risk management principles?

Accepted Answer

Present options for risk treatment including additional controls to reduce residual risk, a time-bound exception with compensating controls, or suspension of the system, and document the board's decision.. ISACA's risk management principles require the risk manager to present decision makers with a full range of treatment options and ensure the chosen path is documented. When residual risk exceeds appetite but business value is high, the appropriate response is a managed exception with compensating controls and a remediation timeline, approved and recorded at the appropriate governance level. This preserves both accountability and business continuity.

Answer

Suspend the AI system immediately because any residual risk exceeding the stated appetite represents an unacceptable control failure.

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Revise the risk appetite statement to accommodate the AI system's residual risk level so the system remains compliant with policy.

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Accept the residual risk because the business value outweighs the risk and the board has ultimate authority to approve exceptions.

Question 13

A startup is preparing to launch a generative AI customer service assistant. Before go-live, the risk team wants to ensure the system has a documented process for handling harmful or inappropriate outputs discovered after deployment. Which practice BEST satisfies this requirement?

Accepted Answer

Establish an incident response process specifically for AI outputs, including detection channels, triage criteria, containment options, and post-incident review.. The requirement is a documented process for handling harmful outputs after deployment, which is precisely what an AI-specific incident response process provides: detection channels, triage criteria, containment options, and post-incident review. Vendor filters, terms of service, and periodic meetings each address adjacent concerns but none establishes the timely, structured response capability the risk team is asking for.

Answer

Add a terms-of-service clause prohibiting users from attempting to elicit harmful outputs, and monitor for violations.

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Schedule a quarterly review meeting where the team will discuss any known issues with the assistant's outputs.

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Rely on the model vendor's content filter, since the vendor is responsible for the safety of outputs generated by their model.

Question 14

A mid-sized insurance company is building its first AI risk inventory. The CISO asks the AI governance lead to define the scope of systems that must be registered in the inventory. Which approach best aligns with AI risk management principles?

Accepted Answer

Register all AI and machine learning systems that inform or make decisions affecting customers, employees, or material business operations.. An AI risk inventory should be scoped by impact, not by technology type or development origin. Registering every system that informs or makes decisions affecting customers, employees, or material operations ensures governance covers predictive and generative models alike, whether built or procured. This enables portfolio-level visibility, dependency analysis, and proportionate oversight. Narrow scoping by novelty, origin, or audit status creates blind spots.

Answer

Register only generative AI systems, since those present the newest and least understood risks.

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Register only models developed in-house, since third-party models are the vendor's responsibility.

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Register systems only after they have completed a full independent audit and received a fairness certification.

Question 15

A global bank's AI governance committee is operationalizing 'AI Risk Appetite' for a portfolio of 40 generative AI use cases. The CISO wants the appetite expressed so that it can be measured continuously against live production telemetry rather than only reviewed annually. Which approach BEST enables continuous measurement of AI risk appetite?

Accepted Answer

Express appetite as quantified thresholds and tolerances on a defined set of AI risk metrics (for example, maximum hallucination rate, bias disparity limits, and incident severity bands), mapped to each use case and monitored through automated pipelines feeding a governance dashboard.. Risk appetite becomes measurable only when it is expressed as quantified thresholds on defined AI risk metrics that automated pipelines can evaluate continuously. Qualitative statements, subjective ratings, and inherited IT appetite all lack the specificity and data feed needed for real-time breach detection. Tying per-use-case thresholds to a governance dashboard preserves proportionality by criticality while giving the committee actionable escalation triggers.

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Adopt the enterprise IT risk appetite statement unchanged and apply it to all AI use cases, relying on the existing IT risk register and periodic internal audit cycles to confirm alignment.

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Define appetite as qualitative statements (for example, 'the bank has low tolerance for reputational harm from AI') and require each use case owner to self-attest compliance in an annual governance questionnaire.

Answer

Set risk appetite at the portfolio level only, using a single aggregate risk score per quarter derived from use case owners' subjective ratings, and escalate to the board whenever the aggregate score exceeds a fixed percentage.