Isaca AAISM Exam Questions Instant Access

Question 1

Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

AHyperparameter tuning

BPrecision

CConfusion matrix

DRecall

Answer : C

A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:

* true positives

* true negatives

* false positives

* false negatives

Precision (B) and recall (D) are derived metrics that use parts of the matrix but do not show the full picture. Hyperparameter tuning (A) is unrelated.

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Question 2

Which of the following is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

AAccess to the model

BProposed regulatory enhancements

CSecurity monitoring and alerting

DBias and ethical practices

Answer : A

When enabling genAI capabilities in a critical system, AAISM prioritizes controlling access to the model and its interfaces (prompt surfaces, context windows, tools/functions, and connected data) because exposure expands the attack surface for prompt injection, data exfiltration, jailbreaks, and misuse. Monitoring (C) is necessary but detective; ethics and bias (D) are vital but secondary to immediate safety and security of a mission-critical environment; proposed regulations (B) are not an immediate operational risk.

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Question 3

When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?

AReview and annotate the AI system's outputs

BImplement identity and access management (IAM)

CConduct human reviews of the AI system's inputs

DFine-tune the system to validate the AI system's inputs

Answer : A

When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls---notably human review and annotation of outputs prior to downstream action---to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.

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Question 4

Which of the following BEST describes the role of risk documentation in an AI governance program?

AProviding a record of past AI-related incidents for audits

BOutlining the acceptable levels of risk for AI-related initiatives

COffering detailed analyses of technical risk and vulnerabilities

DDemonstrating governance, risk, and compliance (GRC) for external stakeholders

Answer : B

In AAISM governance guidance, risk documentation is described as the structured record that defines the organization's risk appetite and tolerance levels for AI initiatives. By outlining acceptable levels of risk, documentation ensures decision-makers can approve, monitor, and adjust AI projects within defined boundaries. While it may also serve audit functions, technical analysis, or communication to stakeholders, its primary role is to formalize risk acceptance thresholds and integrate them into governance and decision-making. This aligns directly with the governance requirement to align AI adoption with organizational risk appetite.

AAISM Study Guide -- AI Governance and Program Management (Risk Documentation and Appetite)

ISACA AI Security Management -- Governance, Risk and Compliance Integration

Question 5

Which of the following is the MOST critical key risk indicator (KRI) for an AI system?

AThe accuracy rate of the model

BThe amount of data in the model

CThe response time of the model

DThe rate of drift in the model

Answer : D

AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.

AAISM Study Guide -- AI Risk Management (Monitoring and Drift Detection)

ISACA AI Security Management -- Key Risk Indicators for AI Systems

Question 6

The PRIMARY goal of data poisoning attacks is to:

Acompromise the confidentiality of output data from the model

Bcompromise the confidentiality of model input data

Cmanipulate the behavior of the model during development

Dundermine the integrity of the AI system's outputs

Answer : D

AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.

Question 7

Which of the following is the MOST serious consequence of an AI system correctly guessing the personal information of individuals and drawing conclusions based on that information?

AThe exposure of personal information may result in litigation

BThe publicly available output of the model may include false or defamatory statements about individuals

CThe output may reveal information about individuals or groups without their knowledge

DThe exposure of personal information may lead to a decline in public trust

Answer : C

The AAISM curriculum states that the most serious privacy concern occurs when AI systems infer and disclose sensitive personal or group information without the knowledge or consent of the individuals. This constitutes a direct breach of privacy rights and data protection principles, including those enshrined in GDPR and other global regulations. While litigation, reputational damage, or loss of trust are significant consequences, the unauthorized revelation of personal information through inference is classified as the most severe, because it directly undermines individual autonomy and confidentiality.

AAISM Exam Content Outline -- AI Risk Management

AI Security Management Study Guide -- Privacy and Confidentiality Risks

Isaca Advanced in AI Security Management AAISM Exam Questions