Free AAISM Exam Braindumps
ISACA Advanced in AI Security Management Questions 71–75 (page: 16)

Updated On: 13-Sep-2026

Which of the following should be a PRIMARY consideration when defining recovery point objectives (RPOs) and recovery time objectives (RTOs) for generative AI solutions?

  1. Prioritizing computational efficiency over data integrity to minimize downtime
  2. Maintaining consistent hardware configurations to prevent discrepancies during model restoration
  3. Preserving the most recent versions of data models to avoid inaccuracies in functionality
  4. Ensuring the backup system can restore training data sets within the defined RTO window

Answer(s): C

Explanation:

For generative AI solutions, the latest model versions capture critical learned patterns and performance improvements. Preserving these ensures that, after a disruption, the AI system can resume accurate and reliable functionality, aligning recovery objectives with operational and business requirements.



Which of the following is the MOST effective use of AI in incident response?

  1. Streamlining incident response testing
  2. Automating incident response triage
  3. Ensuring chain of custody
  4. Improving incident response playbook

Answer(s): B

Explanation:

AI can rapidly analyze alerts, prioritize incidents, and categorize threats, enabling faster and more efficient triage. This reduces human workload, accelerates response times, and ensures that critical incidents receive immediate attention, enhancing the overall effectiveness of incident response.



When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?

  1. Model card
  2. Model prototyping
  3. Hyperparameters
  4. Data quality controls

Answer(s): A

Explanation:

A model card provides transparent documentation of an ML model’s purpose, performance metrics, limitations, and ethical considerations. By clearly communicating this information to stakeholders, it builds trust in the model’s reliability, fairness, and responsible use.



Which of the following BEST represents a combination of quantitative and qualitative metrics that can be used to comprehensively evaluate AI transparency?

  1. AI explainability reports and bias metrics
  2. AI system availability and downtime metrics
  3. AI ethical impact and user feedback metrics
  4. AI model complexity and accuracy metrics

Answer(s): A

Explanation:

Evaluating AI transparency requires both quantitative metrics (like bias scores) and qualitative insights (like explainability reports). This combination provides a comprehensive view of how decisions are made and whether the AI behaves fairly and understandably to stakeholders.



Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?

  1. Limiting employee access to AI tools
  2. Validating the provenance of the data source
  3. Relying on human judgment for oversight
  4. Using a general-purpose large language model (LLM) to detect fraud

Answer(s): B

Explanation:

Ensuring that media and data originate from verified, trusted sources reduces the likelihood that deepfakes can be introduced into systems or decision-making processes. Provenance validation helps detect manipulated content early and mitigates the risk of deepfake attacks effectively.



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