ISACA AAISM Exam Prep
ISACA Advanced in AI Security Management (Page 11 )

Updated On: 13-Sep-2026

Which of the following controls BEST mitigates the risk of bias in AI models?

  1. Regular data reconciliation
  2. Diverse data sourcing strategies
  3. Robust access control techniques
  4. Cryptographic hash functions

Answer(s): B

Explanation:

Bias in AI models often arises from unrepresentative or homogeneous datasets. By sourcing data from diverse populations, contexts, and conditions, organizations can reduce systemic bias, improve fairness, and ensure that the AI model performs equitably across different groups.



Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?

  1. Implementing an automated data cataloging tool that integrates with all organizational data repositories
  2. Creating a centralized team to oversee the classification of data used in AI projects
  3. Conducting quarterly audits of AI data sets for anomalies and missing metadata
  4. Establishing a manual process to categorize data based on business needs and regulatory compliance

Answer(s): A

Explanation:

An automated data cataloging tool provides a centralized, up-to-date inventory of all data assets, automatically classifying data across repositories. This ensures comprehensive visibility, consistency, and accuracy, which is critical for AI development, compliance, and risk management.



An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data.
Which of the following is the MOST effective data cleaning technique to improve model performance?

  1. Applying statistical methods to address missing data and reduce bias
  2. Increasing the frequency of model retraining with the existing data set
  3. Tuning model hyperparameters to increase performance and accuracy
  4. Deleting outlier data points to prevent unusual values impacting the model

Answer(s): A

Explanation:

Using statistical techniques - such as imputation, interpolation, or mean/mode substitution - addresses gaps in the dataset while minimizing bias. This improves the quality of input data, allowing the AI model to generate more accurate and reliable financial forecasts.



Which of the following BEST describes the role of risk documentation in an AI governance program?

  1. Offering detailed analyses of technical risk and vulnerabilities
  2. Demonstrating governance, risk, and compliance (GRC) for external stakeholders
  3. Outlining the acceptable levels of risk for AI-related initiatives
  4. Providing a record of past AI-related incidents for audits

Answer(s): B

Explanation:

Risk documentation in AI governance provides evidence that the organization systematically identifies, assesses, and manages AI-related risks. This transparency supports compliance with regulatory requirements, ethical standards, and stakeholder expectations, reinforcing trust in the organization’s AI practices.



Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?

  1. Weak controls for access to the AI model
  2. Lack of protection against denial of service (DoS) attacks
  3. Inaccurate generalizations from new data by the AI model
  4. Inability to detect input modifications causing inappropriate AI outputs

Answer(s): A

Explanation:

Weak access controls provide adversaries with a direct and relatively easy avenue to manipulate, steal, or misuse the AI model. Controlling access is a fundamental security measure; without it, attackers can exploit the system regardless of other vulnerabilities.



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