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

Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIA™ Study Guide, “problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization.” If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization’s mission or priorities.

Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.

In healthcare AI applications, protecting patient data is critical. The AAIAâ„¢ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).

“Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset.”

While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.

To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIAâ„¢ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.

“Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved.”

While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance—not legal compliance.

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.

Allowing AI to autonomously generate code without human review introduces significant risks, including security vulnerabilities, logic errors, and noncompliance with organizational development standards. The AAIAâ„¢ Study Guide strongly advocates for human-in-the-loop oversight, particularly in automated development contexts.

“AI-assisted development must include manual code reviews to ensure functionality, compliance, and security. Autonomous code generation without validation increases the risk of introducing undetected flaws.”

While A, B, and C involve operational risks or inefficiencies, only D constitutes a direct breach of secure development life cycle principles.

The integrity of data fed into AI systems is a critical concern. The AAIA™ Study Guide emphasizes that validation and filtration processes are essential to mitigate the risk of data poisoning—an attack that can manipulate model behavior by injecting malicious inputs.

“Data poisoning represents a major vulnerability in AI pipelines. Effective controls include robust validation, filtration, and monitoring of training data sources. These preventive practices are essential to ensure model reliability and security.”

While options A, B, and C are important operational and training measures, only D addresses a technical risk that can directly compromise model outputs and trustworthiness.

An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIAâ„¢ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.

“A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence.”

While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.

Neural networks are designed to mimic the way the human brain processes information, enabling AI systems to identify complex patterns and make decisions based on data inputs. The AAIAâ„¢ Study Guide defines neural networks as central to deep learning architectures that emulate cognitive functions.

“Neural networks simulate the interconnectivity and learning processes of the human brain. This capability enables AI systems to handle unstructured data and perform tasks such as image recognition, natural language processing, and predictive analytics.”

Options A, B, and D reflect ancillary benefits but not the core design purpose of neural networks. Thus, C is the most accurate.