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ISACA AAISM Exam Questions
ISACA Advanced in AI Security Management (Page 3 )

Updated On: 3-Jun-2026
Page 3 of 33
PDF with 255 Questions

Which of the following is the BEST approach for minimizing risk when integrating acceptable use policies for AI foundation models into business operations?

  1. Limit model usage to predefined scenarios specified by the developer
  2. Rely on the developer's enforcement mechanisms
  3. Establish AI model life cycle policy and procedures
  4. Implement responsible development training and awareness

Answer(s): C

Explanation:

The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.


Reference:

AAISM Study Guide ­ AI Governance and Program Management (Model Lifecycle Governance)

ISACA AI Security Guidance ­ Policies and Lifecycle Management



Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?

  1. F1 score
  2. Recall
  3. Precision
  4. Specificity

Answer(s): B

Explanation:

AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.


Reference:

AAISM Study Guide ­ AI Technologies and Controls (Evaluation Metrics and Model Performance)

ISACA AI Security Management ­ Model Accuracy and Completeness Assessments



An organization uses an AI tool to scan social media for product reviews. Fraudulent social media accounts begin posting negative reviews attacking the organization's product.
Which type of AI attack is MOST likely to have occurred?

  1. Model inversion
  2. Deepfake
  3. Availability attack
  4. Data poisoning

Answer(s): C

Explanation:

The AAISM materials classify availability attacks as attempts to disrupt or degrade the functioning of an AI system so that its outputs become unreliable or unusable. In this scenario, the fraudulent social media accounts are deliberately overwhelming the AI tool with misleading negative reviews, undermining its ability to deliver accurate sentiment analysis. This aligns directly with the concept of an availability attack. Model inversion relates to reconstructing training data from outputs, deepfakes involve synthetic content generation, and data poisoning corrupts the training set rather than manipulating inputs at runtime. Therefore, the fraudulent review campaign is most accurately identified as an availability attack.


Reference:

AAISM Study Guide ­ AI Risk Management (Adversarial Threats and Availability Risks)

ISACA AI Security Management ­ Attack Classifications



An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls.
Which of the following types of attacks is this an example of?

  1. Prompt injection
  2. Jailbreaking
  3. Remote code execution
  4. Evasion

Answer(s): A

Explanation:

According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.


Reference:

AAISM Exam Content Outline ­ AI Technologies and Controls (Prompt Security and Input Manipulation)

AI Security Management Study Guide ­ Threats to Output Integrity



An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data.
Which of the following is the MOST effective data cleaning technique to improve model performance?

  1. Increasing the frequency of model retraining with the existing data set
  2. Applying statistical methods to address missing data and reduce bias
  3. Deleting outlier data points to prevent unusual values impacting the model
  4. Tuning model hyperparameters to increase performance and accuracy

Answer(s): B

Explanation:

The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction. Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.


Reference:

AAISM Study Guide ­ AI Risk Management (Data Integrity and Quality Controls)

ISACA AI Governance Guidance ­ Data Preparation and Bias Mitigation



Which of the following is MOST important to consider when validating a third-party AI tool?

  1. Terms and conditions
  2. Right to audit
  3. Industry analysis and certifications
  4. Roundtable testing

Answer(s): B

Explanation:

The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.


Reference:

AAISM Exam Content Outline ­ AI Governance and Program Management (Third-Party Governance)

AI Security Management Study Guide ­ Vendor Oversight and Audit Rights



Which of the following is the BEST mitigation control for membership inference attacks on AI systems?

  1. Model ensemble techniques
  2. AI threat modeling
  3. Differential privacy
  4. Cybersecurity-oriented red teaming

Answer(s): C

Explanation:

Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.


Reference:

AAISM Study Guide ­ AI Technologies and Controls (Privacy-Preserving Techniques)

ISACA AI Security Management ­ Membership Inference Mitigations



Which of the following types of testing can MOST effectively mitigate prompt hacking?

  1. Load
  2. Input
  3. Regression
  4. Adversarial

Answer(s): D

Explanation:

Prompt hacking manipulates large language models by injecting adversarial instructions into inputs to bypass or override safeguards. The AAISM framework identifies adversarial testing as the most effective way to simulate such manipulative attempts, expose vulnerabilities, and improve the resilience of controls. Load testing evaluates performance, input testing checks format validation, and regression testing validates functionality after changes. None of these directly address the manipulation of natural language inputs. Adversarial testing is therefore the correct approach to mitigate prompt hacking risks.


Reference:

AAISM Exam Content Outline ­ AI Risk Management (Testing and Assurance Practices)

AI Security Management Study Guide ­ Adversarial Testing Against Prompt Manipulation



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