AAISM Practice Exam 3 — 20 Questions

Question 1

A financial services firm deploys a gradient-boosted credit-scoring model served behind an internal REST endpoint. The security team wants to detect whether an attacker is probing the model to reconstruct its decision boundaries, but the endpoint does not log request payloads. Which control should be implemented FIRST to enable this detection?

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

Accepted Answer

Capture full inference request and response telemetry, including feature vectors, timestamps, and caller identity, into a tamper-evident store.. Model extraction detection is fundamentally a monitoring problem: you must observe the sequence and shape of queries reaching the model. The endpoint's gap is that it discards payloads, so no downstream analytics can identify systematic probing. Capturing inference requests and responses with caller identity and timestamps restores that visibility, while validation, privacy training, and mutual TLS all address different concerns and leave the detection gap open.

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Apply differential privacy during the next retraining cycle to add calibrated noise to the model's outputs.

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Enforce mutual TLS between the calling application and the inference service to authenticate every client.

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Enable schema-based input validation that rejects any feature vector containing out-of-range or unexpected values.

Question 2

A company uses a large language model (LLM) to generate code for its software development team. The security team is concerned that the LLM might inadvertently suggest code containing vulnerabilities. Which control should be implemented to mitigate this risk?

Accepted Answer

Implement a static application security testing (SAST) tool that scans the LLM-generated code for known vulnerabilities before it is committed.. The most effective control to mitigate the risk of vulnerable code from an LLM is to implement SAST that scans generated code before it is committed. SAST provides a deterministic, rule-based check that can catch known vulnerabilities, acting as a critical safety net. While fine-tuning, prompt engineering, and curated libraries can help, they are not foolproof and do not provide the same level of assurance as automated scanning.

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Use prompt engineering to instruct the LLM to always generate secure code.

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Fine-tune the LLM on a dataset of secure code examples to bias it toward safe coding practices.

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Restrict the LLM to only suggest code from a curated library of pre-approved secure snippets.

Question 3

A company is deploying a computer vision model for quality control on a manufacturing line. An attacker gains physical access to the camera feed and subtly alters a few pixels in the images to cause the model to misclassify defective products as non-defective. Which type of attack is this?

Accepted Answer

Adversarial example attack. An adversarial example attack occurs when an attacker intentionally modifies input data at inference time to cause the model to produce incorrect outputs. Here, the attacker alters camera feed pixels to make defective products appear non-defective. This is a direct example of an adversarial example attack, which is a key concern in AI security and requires defenses like input validation and robust model training.

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Evasion attack

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Poisoning attack

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Model inversion attack

Question 4

An organization is selecting a control to protect an AI model's integrity after it is deployed to a model registry. They want to ensure that any unauthorized modification to the model artifact is detectable before it is loaded into production. Which control should they implement?

Accepted Answer

Cryptographic hashing of the model artifact with signature verification at deployment time.. Cryptographic hashing with signature verification provides a proactive integrity check that detects any modification to the model artifact before it is loaded into production. The other controls address access management, confidentiality at rest, or post-deployment behavior, none of which verify artifact integrity at deployment time.

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Monitoring inference latency and accuracy metrics after deployment.

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Encrypting the model artifact at rest with a customer-managed key.

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Role-based access control on the model registry to limit who can upload new versions.

Question 5

An organization's AI governance committee is defining how retrieval-augmented generation assistants should handle ingested documents. A security analyst proposes that each retrieved chunk carry metadata identifying its source system, ingestion time, and sensitivity label, and that the assistant's responses cite the chunks used. Which objective does this control primarily serve?

Accepted Answer

Enabling traceability of generated content back to authoritative sources and enforcing sensitivity-aware handling.. Tagging retrieved chunks with source, time, and sensitivity, and surfacing citations, creates an auditable chain from answer back to document. That supports investigating incorrect or harmful outputs, verifying which authoritative source was used, and applying classification-based handling. It is a governance and traceability control, distinct from generation quality, storage optimization, and injection defense.

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Preventing prompt injection by sanitizing the content of retrieved documents before embedding.

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Improving the fluency and grammatical quality of generated responses.

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Reducing the vector database storage footprint by deduplicating embeddings.

Question 6

What is the primary risk associated with 'Shadow AI' in an organization?

Accepted Answer

Unmonitored data leakage and compliance risk. Shadow AI refers to AI tools, models, or services used by employees without IT or security approval, and its primary risk is that data flows into these unsanctioned systems without monitoring, DLP controls, or compliance review. This creates exposure to data leakage, regulatory violations (e.g., GDPR, HIPAA), and loss of visibility into where sensitive information resides. Cost savings, accuracy gains, and faster development are potential benefits, not risks.

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Lower cloud costs

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Increased model accuracy

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Faster code development

Question 7

A financial services firm uses a third-party large language model for internal research summarization. The vendor publishes a model card but will not disclose training data composition, and the firm's regulator has begun asking how the organization would detect and respond if the model began producing outputs that subtly favor one issuer's securities. The CISO wants a control that provides early warning without requiring vendor source code. Which approach is most appropriate?

Accepted Answer

Deploy an output monitoring pipeline that scores responses for issuer sentiment drift against a frozen baseline and routes statistical anomalies to an AI risk owner for investigation.. When a vendor will not disclose training data and source code is unavailable, the organization must monitor observable behavior. Scoring outputs for issuer sentiment drift against a frozen baseline creates a measurable, repeatable signal that can be automated and escalated. This satisfies the regulator's expectation of ongoing detection while respecting the vendor's opacity, and it keeps accountability with the firm's own AI risk owner.

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Restrict the model to summarization of documents that already contain an explicit issuer rating, so that any bias in free-text generation cannot influence downstream decisions.

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Request that the vendor provide a signed attestation that the model does not favor any issuer and retain the attestation as evidence of due diligence.

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Fine-tune the vendor model on the firm's own curated research corpus so that issuer preferences in the base model are overwritten by internal data.

Question 8

A global bank has deployed a generative AI assistant to its wealth management advisors. The AI security manager must establish an oversight process that ensures the model's outputs remain aligned with the bank's risk appetite as market conditions and regulations evolve. Which ongoing activity should be the PRIMARY mechanism for this alignment?

Accepted Answer

Implementing a continuous AI risk monitoring and reporting program with defined KRIs and periodic model performance reviews.. Aligning an AI system with evolving risk appetite requires ongoing, measurable oversight, not one-time or static controls. A continuous risk monitoring and reporting program with defined KRIs and periodic model performance reviews directly connects live AI behaviour to governance thresholds, enabling detection of drift and timely escalation. Penetration tests, attestations, and archiving are valuable but do not provide the continuous linkage needed for sustained alignment.

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Archiving all AI-generated client communications for seven years to satisfy recordkeeping requirements.

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Requiring wealth advisors to sign an annual attestation that they have read the AI acceptable use policy.

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Conducting an annual third-party penetration test of the AI model's API endpoints.

Question 9

A multinational retailer is expanding its AI governance program to cover AI systems developed by its regional subsidiaries. The central AI governance office wants to ensure that subsidiary-developed AI systems are consistently registered, risk-assessed, and monitored. Which two governance mechanisms best achieve this consistency? (Choose two.)

Accepted Answer

A federated AI governance model in which each subsidiary appoints an accountable AI risk owner who applies centrally defined risk assessment criteria and reports into the central AI inventory.. Consistency across subsidiaries is best achieved by combining a federated governance model with accountable local AI risk owners and a shared governance platform that standardizes intake, risk assessment, and monitoring. The federated model supplies local accountability and context, while the platform enforces common data structures and gives the central office enterprise-wide visibility. Together they cover registration, assessment, and monitoring without centralizing every decision.

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An annual attestation from each subsidiary's CIO that all AI systems comply with the central AI policy.

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A central mandate requiring every subsidiary AI project to be approved by the central AI governance office before any development begins.

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A requirement that all subsidiary AI systems use the same foundation model provider as the parent company.

Question 10

A healthcare organization is deploying an AI system to triage patient symptoms. The AI governance committee is reviewing the program's compliance with the organization's AI ethics principles. Which of the following is the MOST effective way to ensure that the AI system's decisions are explainable to clinicians and patients?

Accepted Answer

Implement a post-hoc explanation tool that provides feature importance scores for each AI recommendation.. Implementing a post-hoc explanation tool provides feature importance scores that clarify why a specific recommendation was made, directly supporting explainability for clinicians and patients. The other options either sacrifice accuracy, provide irrelevant technical details, or offer only confidence levels without reasoning, failing to meet the ethical requirement for decision-level transparency.

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Provide clinicians with a detailed technical report of the model's architecture and training data.

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Use a simple, inherently interpretable model like a decision tree instead of a complex deep learning model.

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Ensure the AI system's output includes a confidence score for each recommendation.

Question 11

A healthcare insurer deploys an AI model to prioritize prior-authorization requests. During post-deployment monitoring, the AI governance lead discovers the model's denial rate for one demographic group is materially higher than for others, though overall accuracy meets the target. Which action should the governance lead take FIRST?

Accepted Answer

Document the finding, validate the disparity with a fairness assessment, and escalate through the AI risk escalation process for a remediation decision.. When monitoring reveals a material fairness concern, governance requires validation and escalation before remediation. The disparity may stem from data quality, proxy variables, or genuine model bias, and only a structured assessment can determine the appropriate response. Escalating through the defined risk process ensures legal, compliance, and clinical stakeholders make an informed, documented decision rather than reacting prematurely.

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Adjust the model's decision threshold until denial rates are equal across all demographic groups.

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Take no action because overall accuracy meets the target and the disparity may reflect legitimate clinical differences.

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Immediately retire the model and revert to fully manual review for all prior-authorization requests.

Question 12

A financial services firm uses a third-party large language model via API for customer-facing investment guidance. The AI risk manager is told that the vendor has updated the model weights without changing the API version number or notifying customers. The firm's own testing now shows different outputs for the same regulated prompts. Which risk management control failure does this scenario MOST directly illustrate?

Accepted Answer

Inadequate third-party AI supply chain transparency and change notification requirements in the vendor contract.. When an organization consumes a third-party model, its risk posture depends on the provider's change management practices. Silent weight updates invalidate prior validation, testing, and regulatory attestations. The most direct control failure is the absence of supply chain transparency: contracts must require advance notice of material model changes, and technical measures such as version pinning or pinned endpoints must ensure the organization can detect and revalidate changes before they affect production.

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Insufficient adversarial robustness testing against prompt injection attacks on the API endpoint.

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Failure to implement differential privacy on the prompts sent to the third-party model.

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Lack of model explainability tooling to interpret why outputs changed after the update.

Question 13

A global manufacturer is standing up an AI governance committee. The CISO wants to ensure the committee can make defensible decisions about high-risk AI deployments. Which TWO responsibilities are MOST essential for this committee to own directly? (Choose two.)

Accepted Answer

Resolving escalated AI risk issues and directing remediation when monitoring or audit identifies unacceptable risk.. A governance committee's essential authorities are approving high-risk use cases against defined criteria and resolving escalated risk issues with documented decisions. These responsibilities ensure accountability, consistency with risk appetite, and auditability. Implementation, daily monitoring, and contract negotiation are execution activities that belong to engineering, operations, and procurement, and assigning them to the committee would undermine its oversight independence.

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Performing daily operational monitoring of model drift and data pipeline health for every deployed model.

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Negotiating pricing and service-level agreements with third-party AI model providers.

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Writing and maintaining the production code for all AI models used in manufacturing operations.

Question 14

A software company's AI governance team is defining metrics to report to the board quarterly. The board wants assurance that AI risks are being managed, not just a list of models deployed. Which metric set BEST supports that objective?

Accepted Answer

Percentage of high-risk AI systems with completed impact assessments, open high-severity findings and their aging, and incident trends by severity.. Board reporting should demonstrate control effectiveness against risk appetite, not just activity volume. Metrics such as impact assessment completion for high-risk systems, aging of high-severity findings, and incident trends by severity reveal whether controls are working and where exposure remains. They allow the board to challenge management, allocate attention, and confirm that AI risk is being managed rather than merely monitored.

Answer

Number of AI vendors onboarded and average contract value of AI-related purchases.

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Total training hours delivered on AI ethics and employee satisfaction scores with AI tools.

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Count of AI models in production and total number of AI-related help desk tickets.

Question 15

An enterprise has deployed a third-party AI-powered hiring screening tool. The vendor recently updated its model, and the organization's HR team was not notified beforehand. The AI governance committee needs to ensure that such changes are managed consistently going forward. Which action should the committee take FIRST?

Accepted Answer

Add a contractual clause requiring advance notification and review of material model changes before deployment.. The governance committee must close the gap that allowed an unannounced vendor model change to affect hiring. A contractual requirement for advance notification and review creates a repeatable control, ensuring the organization can evaluate risks and performance before changes go live. This approach balances operational continuity with oversight, directly addressing the third-party change management deficiency without overreacting or neglecting the root cause.

Answer

Immediately terminate the vendor relationship and build an in-house model.

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Require the vendor to provide the model's source code for internal review.

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Instruct HR to manually re-screen all candidates processed since the update.

Question 16

A multinational corporation is establishing an AI governance program. The board has asked the AI security manager to identify the essential components that must be in place to ensure the program can enforce accountability across business units. (Choose two.)

Accepted Answer

A defined AI governance structure with clear roles and responsibilities, including an accountable executive.. Enforcing accountability requires both a clear governance structure with assigned responsibility and a centralized inventory of AI models with risk assessments. The governance structure provides authority and decision rights, while the inventory provides the visibility needed to track and oversee AI use. Communication tools, technology mandates, and surveys may support the program but do not establish the enforcement mechanisms essential for accountability.

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A requirement that all AI projects use the same programming language and cloud provider.

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An annual employee satisfaction survey that includes questions about AI tools.

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A monthly newsletter highlighting AI success stories from each business unit.

Question 17

A company is developing an internal LLM-based tool. Which activity is required for 'Accountability'?

Accepted Answer

Maintaining a log of model development and decision-making.. Accountability in AI governance means being able to demonstrate who did what, when, and why across the model lifecycle. Maintaining a log of model development and decision-making (C) creates the evidentiary record that supports traceability, audit, incident investigation, and regulatory response. Without such records, an organization cannot prove that its AI was developed and operated responsibly, which is the essence of accountability.

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Deleting all documentation.

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Limiting access to the CEO.

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Outsourcing to a third party.

Question 18

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 19

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 20

A manufacturing firm's AI governance lead is designing the enterprise AI policy hierarchy. The board has approved a high-level AI principles statement. Business units now need actionable rules for selecting and deploying vendors that supply AI-enabled equipment. Which artifact should the governance lead create next?

Accepted Answer

A detailed AI vendor risk assessment standard with mandatory evaluation criteria, evidence requirements, and approval gates for AI-enabled suppliers.. A policy hierarchy moves from principles to standards to procedures, and the gap described is the standard layer. A vendor risk assessment standard provides mandatory, auditable criteria and approval gates that business units can apply consistently. Revising principles, delivering training, or building a monitoring dashboard each fails to supply the actionable selection criteria the scenario requires.

Answer

A quarterly vendor performance dashboard reporting incident counts for AI-enabled equipment already deployed across plants.

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A training deck explaining the board's AI principles so that procurement staff can interpret them when negotiating vendor contracts.

Answer

A revised AI principles statement with additional clauses addressing vendor responsibility and ethical sourcing.