Isaca AAIA - ISACA Advanced in AI Audit (AAIA)

AI-related incidents often differ significantly from traditional IT incidents due to their dependence on data, model behavior, and algorithm performance. According to the AAIAâ„¢ Study Guide, incident management programs must include capabilities specifically tailored to AI, such as detecting and mitigating model drift and safeguarding against data poisoning or integrity attacks.

“AI incident response frameworks must account for issues unique to machine learning, including model drift, adversarial inputs, and data integrity breaches. An effective program incorporates detection, response, and recovery mechanisms for these AI-specific threats.”

While options A and B contribute to improving incident response over time, and option D suggests best-practice alignment, only option C directly addresses active response capabilities for high-risk, real-time AI vulnerabilities.

Imputing missing values using the mean, median, or mode is a common technique to fill blanks in datasets. However, according to the AAIAâ„¢ Study Guide, this method introduces a significant risk of information distortion, especially if the data is not normally distributed or if imputed values disproportionately impact underrepresented groups.

“Simple imputation can reduce data variability and reinforce existing biases. It may also misrepresent the true distribution of the data, leading to skewed model outputs.”

Other options (B, C, D) may involve transformations, but they do not inherently cause as much bias or data misrepresentation as A.

To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIAâ„¢ Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.

“Limiting review input to authenticated users restricts unauthorized actors—such as competitors or bots—from submitting biased or harmful data. This control protects model training and outputs from being manipulated.”

Options A and C address technical data handling but not source authenticity. Option B may inadvertently incentivize biased reviews. Option D provides a robust control against poisoning.

LIME (Local Interpretable Model-Agnostic Explanations) is a leading tool for explaining individual AI predictions by approximating the behavior of complex models with simple, interpretable ones in localized regions. The AAIAâ„¢ Study Guide highlights LIME as highly effective for providing transparency and interpretability to non-technical stakeholders.

“LIME enables auditors to demonstrate how specific input features influenced an AI decision, facilitating trust and stakeholder understanding—especially in regulated or high-impact contexts.”

Options A, B, and C are technical modeling techniques but do not prioritize stakeholder-friendly explanation. Therefore, D is best for transparency.

Bias in AI models is most commonly introduced through the training data. The AAIAâ„¢ Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.

“The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results.”

While adaptability (C) and model parameters like temperature (D) affect behavior, they do not address the root cause of most biases. The development environment (B) supports infrastructure but not ethical assurance.

Accountability for data management (option D) is the most crucial consideration. The AAIA™ Study Guide emphasizes that “clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls.”

Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.

Large Language Models (LLMs) have the capacity to memorize and unintentionally reveal sensitive information if not properly managed. As per the AAIAâ„¢ Study Guide, data masking is a critical technique that prevents the exposure of personally identifiable information (PII) or confidential content by obscuring or replacing sensitive parts of the data during training or interaction.

“Data masking ensures that training data used for LLMs does not contain real sensitive identifiers. Unlike sanitization, masking modifies data to maintain utility while eliminating exposure risk.”

Manual monitoring and access controls are supportive security measures, and data sanitization helps remove content but may not preserve the data’s structure. Data masking offers the most proactive and technically robust solution.

The ethical use of customer data is rooted in respecting privacy, maintaining informed consent, and enabling data subjects to exercise control over their personal information. The AAIAâ„¢ Study Guide clearly outlines that obtaining explicit consent and providing opt-out capabilities align with principles of data protection and ethical AI.

“Ethical AI implementation includes transparency in data collection, clear consent mechanisms, and the right of users to opt out or control their personal data usage. Retail and consumer applications must ensure that personalized services do not override these data subject rights.”

Options B and D violate principles of minimal data use and consent, while C may create unnecessary privacy exposure. Therefore, A is the correct and most ethical practice.

Generative AI tools such as large language models often have token limitations that restrict how much input they can process in a single prompt. According to the AAIA™ Study Guide, when input exceeds token limits, the model processes only the initial portion—leading to biased outputs based on early entries.

“Token limits in generative AI systems may lead to partial input processing, skewing model outputs toward the beginning of the data and introducing interpretive bias. Auditors must assess whether output reflects the full dataset.”

Options A and C describe side effects, but D identifies the most significant risk—output distortion due to truncated input.