Isaca Updated AAISM Exam Questions and Answers by kira

An AI impact assessment prioritizes alignment with stated business objectives and intended purpose of the AI use case. Establishing whether the AI system measurably supports defined objectives (value, outcomes, success criteria) is the primary lens before assessing secondary concerns (workforce effects, awareness initiatives, reputational considerations). Governance requires confirming purpose limitation, success metrics, and risk–benefit trade-offs tied to organizational objectives; only then are downstream impacts evaluated.

The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor’s compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards. Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.

According to AAISM study guidance, the most direct and effective way to obtain large volumes of diverse data for application testing is through open-source data repositories. These repositories provide freely available, well-documented, and often standardized data that supports testing and benchmarking in a compliant manner. Model cards document AI behavior but do not provide data. Incorporating search content may introduce legal, privacy, and quality risks. Data augmentation is useful for expanding existing sets but does not provide the breadth or size required when starting with insufficient data. The recommended best practice for sourcing large testing datasets is therefore the use of open-source repositories.

In the operational phase, AAISM emphasizes continuous monitoring of models for performance, stability, robustness, drift, data quality, security events, and policy compliance. This includes telemetry, thresholds, alerts, incident response for AI failures, and evidence collection for audits. Alignment to business needs is established earlier in planning/governance; algorithmic optimization and feedback collection are supporting activities, but the primary operational objective is live monitoring and assurance to keep risk within tolerance.