Isaca ISACA Advanced in AI Audit (AAIA) AAIA Exam Dumps: Updated Questions & Answers (December 2025)

When an AI system begins givingincorrect financial advice, the FIRST step is tosuspend the chatbot(D) to prevent ongoing harm. AAIA emphasizes protecting users from unsafe decisions as the top priority in AI operations.

Suspension allows the organization to:

Stop distribution of harmful advice

Assess impact and identify faulty API dependencies

Prevent regulatory or customer harm

Conduct root-cause analysis safely

Retraining (C) is corrective but must occurafterassessment. Adding rules (B) risks masking deeper issues. Speed patches (A) are irrelevant to correctness.

Using AI to make employment decisions such as driver termination or retention introduces significant ethical risks. If based solely on performance metrics without context or human review, such systems can lead to unfair treatment or discrimination—violating principles of transparency and due process.

“Automating workforce decisions must be approached cautiously to prevent discriminatory outcomes. Ethical AI governance requires oversight when AI is used for employment-impacting decisions.”

A, C, and D involve business optimization without directly affecting individual employment rights. Therefore, B poses the greatest ethical risk.

Data poisoningoccurs when attackers manipulate training data to corrupt model behavior. The most direct mitigation is to implementrobust data validation and integrity checks(option C), including anomaly detection on input distributions, provenance controls, verification of data sources, and safeguards for pipelines feeding the training set. AAIA highlightsthreats and vulnerabilities specific to AIand the importance of controls that protect data integrity in AI Operations.

Option A (relying on third-party providers) does not inherently eliminate poisoning risk; providers themselves may be vulnerable. Option B (increasing data size) can dilute but not reliably remove malicious samples. Option D (simpler algorithms) might help interpretability but does not directly prevent poisoned data from influencing the model. The most effective, aligned control isrigorous data validationto ensure only trustworthy data enters the training process.

In the case of a potential data exposure through AI model outputs, the first and most responsible action from an auditing and risk standpoint is to halt further risk propagation. According to the AAIA™ Study Guide, immediate containment is vital, especially when regulatory and reputational risks are high.

“Upon detection of a data breach risk, AI models should be immediately disabled from public or partner use, and all relevant parties should be notified as part of a responsible disclosure and containment strategy.”

While options A and D are longer-term remediation steps and B is investigative, none of them provide the urgent containment that is best practice in such a breach context.

Bias in AI decision-making is one of the most critical risks, particularly when AI influences areas like hiring, lending, or healthcare. The AAIA™ Study Guide highlights the ethical and operational consequences of biased models, which may lead to discrimination, legal liability, and reputational damage.

“Bias in AI outputs can originate from skewed training data or flawed algorithms. Auditors must evaluate whether mitigation techniques, such as bias detection and fairness testing, are in place.”

While costs (A) and industry maturity (B) are considerations, they do not pose the same systemic ethical risk. Resistance (D) is a change management issue. C represents the most impactful and widespread risk.

Data drift occurs when the statistical properties of input data change over time, causing the AI model to produce increasingly inaccurate or biased results.

The correct response is todocument the risk and implement continuous monitoring(A) because:

Drift requiresongoing detection, not a one-time fix

Retraining too early can reinforce issues (especially with the same dataset)

Disabling the system (D) is overly disruptive unless drift results in unsafe decisions

Continuing deployment without monitoring (C) increases risk of degraded performance

AAIA emphasizesdrift monitoring dashboards, alerting mechanisms, and retraining triggersas essential controls for AI operations.

The strongest demonstration of compliance with privacy principles—especially those emphasized in AAIA, such aslawfulness, transparency, and consent—is obtainingexpressed customer consent(B) before sharing data with third parties. Consent ensures that data processing aligns with legal and ethical requirements, reducing risks related to regulatory violations, unauthorized processing, and loss of customer trust.

Option A and C relate only to disclosure and transparency, which are necessary but insufficient. Option D concerns vendor security controls, which are important but do not address the legal basis for sharing data.Explicit, informed consentis the highest standard of compliance for third-party data sharing.

Ordinal encoding assigns numeric values in an order (e.g., Poodle = 1, Labrador = 2, Husky = 3). This falsely implies hierarchy or ranking among breeds, introducing bias into the model.

AAIA emphasizes that incorrect encoding of categorical variables can cause:

Artificially weighted importance

Misleading correlations

Incorrect predictions based on false orderFor non-ordered categories,one-hot encoding(D) is the correct, low-risk approach.Lack of scaling (A) is not directly relevant for categorical fields. Clustering (B) is unrelated.Thus, ordinal encoding introduces the highest risk.

Recurrent neural networks (RNNs) and their variants (such as LSTMs and GRUs) are designed to handlesequential data, capturing dependencies across time or position in a sequence. In language translation, words and phrases must be interpreted in context, where the meaning of a word depends on preceding (and, in advanced architectures, following) tokens. RNNs maintain internal state across steps, allowing the model to encode information from earlier parts of the sentence when predicting later outputs.

Option B (association rules) refers more to classical data‐mining methods, not the core reason RNNs work for translation. Option C (grid data) is more relevant to convolutional neural networks used for images. Option D (unidirectional) is not inherently an advantage; in fact, bidirectional models are often preferred. Therefore, the key property enabling RNN use in translation is thesequential processingcapability.

Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide, "algorithmic transparency—enabling auditors and stakeholders to see and understand the rationale behind automated decisions—is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements."

Transparent justification of decisions (option C) directly supports an auditor’s ability to evaluate the appropriateness, logic, and fairness of the AI’s outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.

By contrast, options A, B, and D are either standard operational aspects or supportive features, but they do not directly empower auditors to evaluate or scrutinize the algorithm’s decision-making process. The Study Guide explicitly highlights, “Effective AI governance frameworks require that systems provide clear, interpretable explanations for their decisions, supporting auditability and accountability across the AI lifecycle.”