Testing the AI model with a curated and representative sample data set allows auditors to directly evaluate the fairness and bias of model decisions. This approach is aligned with best practices outlined in the AAIA™ Study Guide, as it enables quantifiable analysis of model behavior across different demographics or input scenarios.
"To assess fairness, auditors should use controlled data sets to evaluate whether model outputs disproportionately impact specific groups. This empirical testing provides stronger evidence than qualitative methods." While metadata (A) and developer interviews (C) can supplement findings, only B provides objective, reproducible evidence. Option D may reflect real-world interactions but lacks the control and consistency required in an audit.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Fairness and Bias Testing in AI Systems"

Sudden and unexplained changes in AI-generated credit scores may result from data drift, model overfitting, or lack of recalibration. According to the AAIA™ Study Guide, regular expert review and calibration help maintain model reliability and transparency, particularly in high-stakes decisions like credit scoring.
"Ongoing human oversight ensures that predictive models remain stable and justifiable. In high-impact environments, such as banking, experts must review and recalibrate AI systems to prevent opaque or unexpected behavior." Option B may cause the exclusion of relevant long-term patterns. C promotes risk by removing oversight. D is a validation strategy, not a stability control. Therefore, A is the best option.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Monitoring and Recalibration Strategies"

Prompt injection attacksinvolve maliciously crafted inputs intended to override system instructions, exfiltrate data, or cause harmful behavior. The most effective control aligned with incident management is to deploy robust input validation and sanitization(C), which includes rules and filters designed to detect and neutralize potentially malicious content before it reaches the model. AAIA's coverage of AI threats and vulnerabilities highlights the importance ofinput validation and secure prompt handlingfor generative AI systems.
Fine-tuning the model (A) is a long-term adaptation, not an immediate incident control. Scanning for code- like structure (B) or excessive special characters (D) may catch some attacks but are too narrow; many prompt injections use natural language. Comprehensiveinput validationand sanitization is the most effective and generalizable incident management response.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI; AI-specific threats and incident management.
ISACA AI security guidance covering prompt injection and input validation controls.

The most effective way to protect proprietary AI model components-architecture, weights, hyperparameters-is throughstrict access controls and encryption(option D).
AAIA identifies model theft and reverse engineering as high-severity security risks, especially for organizations whose competitive advantage relies on intellectual property embedded in AI models.
Proper controls include:
* Role-based access control (RBAC)
* Encryption at rest and in transit
* Model-level access segregation
* Hardware security modules (HSMs)
* Zero-trust access principles
Training (A) increases awareness but does not enforce protection.
Confidentiality agreements (B) are legally binding but do not stop technical intrusions.
Audit logs (C) track training data but do not secure the model itself.
Thus, encryption and access control provide the strongest protection.
References:
AAIA Domain 2: AI Security and Intellectual Property Protection
AAIA Domain 1: Access Control and Safeguards

AAIA guidance states thatfairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, thefirstcourse of action is to review training data (option B) to identify:
* Skewed demographic distributions
* Missing or underrepresented populations
* Inappropriate use of sensitive attributes
* Incorrect labels or improperly encoded variables
* Historical decisions that may propagate discrimination
Only after identifying the existence and nature of bias can an organization move on to remediation steps such as retraining (C) or comparing alternate model behavior (A).
Allowing customers to contest premiums (D) is a post-decision remedy, not a fairness evaluation step.
AAIA emphasizes addressing bias at theroot-the training data-before adjusting the model or outputs.
References:
AAIA Domain 5: Fairness, Bias Identification, Ethical AI Principles
AAIA Domain 2: Data Governance and Input Evaluations