Isaca AAISM Dumps Questions Answers

Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage—directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. “Ensure responsible use” is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.

The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data. According to the AI Security Management™ (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased—reflecting skewed representation, sampling imbalance, or embedded prejudice—the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.

Why Option A is Correct:

Systemic bias affects the integrity and trustworthiness of AI-generated statistical information.

It can introduce discriminatory outcomes, ethical breaches, and regulatory non-compliance—key concerns in AAISM’s AI Risk Management and Governance principles.

Mitigating systemic bias requires data quality assessments, fairness audits, bias detection tools, and model interpretability measures to ensure the derived insights are accurate and ethically sound.

Why Other Options Are Incorrect:

Option B: Incomplete outputs can affect accuracy but are typically handled through process monitoring or retraining, not as a primary risk factor in statistical validity.

Option C: Lack of data normalization is a technical preprocessing issue, not a governance-level risk impacting statistical trustworthiness.

Option D: Hallucinations occur mainly in generative models (e.g., LLMs) and affect content generation, not statistical computation pipelines.

Exact Extract from Official AAISM Study Guide:

“Systemic bias in AI training and inference data represents the most material statistical risk. Bias propagates through derived metrics, predictive models, and decision outputs, compromising fairness, accuracy, and compliance. AI Security Management requires implementing bias detection, fairness testing, and governance mechanisms to identify and mitigate such systemic bias before using AI-generated analytics for organizational or regulatory reporting.”

AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:

• model behavior

• compliance gaps

• security improvements

• governance alignment

Internal auditors (B) perform assessments but do not implement changes. System architects (A) support technical fixes but do not own compliance. End users (C) are not responsible for audit remediation.