AAIA Exam Dumps - ISACA Advanced in AI Audit (AAIA)

A significantly higher rejection rate is a clear indicator of potentialalgorithmic discrimination. Management’s PRIMARY response should be to conduct acomprehensive bias analysis(C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.

Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help butonly afterthe root cause is identified—it is not the primary first step.

When AI models are updated frequently in production,continuous monitoringis critical to detect performance degradation, bias drift, hallucinations, and security issues introduced by new versions. A lack of continuous monitoring (option C) means the organization might not promptly detect harmful behaviors or compliance violations, despite frequent changes, exposing it to operational, reputational, and regulatory risk.

Option A (no AI-specific change management) is serious but can be partially mitigated if effective monitoring reveals issues quickly. Option B (overreliance on manual review) is inefficient but still a control. Option D (no dedicated AI governance committee) is a structural weakness, yet the immediate operational risk is greatest where model changes are not constantly observed. AAIA emphasizessupervision of AI solutionsandmonitoring of outputs and impacts, which are directly undermined when continuous monitoring is absent.

The AAIAâ„¢ Study Guide stresses that inputting confidential or proprietary data into third-party generative AI tools may result in unauthorized data disclosure. These tools may store, process, or retrain on the input data, leading to privacy and intellectual property risks.

“When employees input sensitive data into external AI tools, organizations risk losing control over that information. This may result in regulatory non-compliance, legal exposure, and irreversible data leakage.”

While business disruption (C) and reliance (D) are notable, the most severe and immediate risk is B—unauthorized disclosure. Data contamination (A) impacts model reliability, not data security.

The AAIAâ„¢ Study Guide recommends using validation tools to fine-tune and evaluate ML models, particularly when high false positives undermine operational efficiency. ML validation can identify threshold adjustments, retraining needs, or feature misweighting contributing to excessive alerting.

“Model validation enables organizations to quantify performance, reduce false alarms, and recalibrate AI behavior to align with operational needs and threat landscapes.”

While logs (A) and benchmarks (B) help with diagnosis, they don’t improve the model. Penetration testing (C) evaluates detection, not alert noise. D is the most effective solution.

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.

Evaluatingedge cases—rare but critical scenarios where AI may behave unpredictably—must be done during thetest and verifystage (D). This phase is designed to simulate extreme or unusual inputs, validate performance under stress, and ensure robustness and safety before deployment. AAIA highlights thatrobustness testing, including edge case evaluation, is a key testing technique for AI solutions.

While planning (A) and data collection (C) prepare inputs, they do not evaluate model behavior. Operating and monitoring (B) may detect issues later, but edge case testing must occurpre-deploymentto reduce risk.

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.

Fororganization-wide adoptionof AI, the MOST important change management consideration is that the organization hassuitable data governance and infrastructure readiness(D). Without robust data governance, AI systems may rely on poor-quality, noncompliant, or insecure data; without appropriate infrastructure (compute, storage, monitoring, integration), implementations will be fragile, unreliable, and risky. AAIA stresses thatAI readinessincludes governance structures, data stewardship, and technical foundations.

Senior leadership involvement (A) is critical for sponsorship and culture, but even strong leadership cannot compensate for fundamentally inadequate data governance and infrastructure. Option B (shorter training cycles) focuses on user adoption but not core risk and control issues. Option C (phased implementation and stage gates) is good project discipline, but still depends on the underlying readiness. Therefore, from an AI risk and governance perspective, ensuringdata governance and infrastructure readinessis the primary prerequisite for successful, safe change management in AI adoption.