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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q423-Q428):

NEW QUESTION # 423
After an organization deploys an AI-based system, a regulator warns of increased risk related to AI re-identification attacks on anonymized datasets. Which of the following should the information security manager do FIRST?

Answer: A

Explanation:
Establishing continuous monitoring with privacy audits and adversarial testing directly addresses the evolving risk of re-identification by detecting vulnerabilities and measuring the effectiveness of anonymization over time. This proactive approach enables the organization to assess and mitigate the risk without unnecessarily disrupting operations.


NEW QUESTION # 424
A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications. Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision-making and prevent bias?

Answer: A

Explanation:
In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.
The AI Security ManagementTM (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the-loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.


NEW QUESTION # 425
A CISO has been tasked with providing key performance indicators (KPIs) on the organization's newly launched AI chatbot. Which of the following are the BEST metrics for the CISO to recommend?

Answer: C

Explanation:
For executive security and governance reporting, AAISM prioritizes risk- and harm-oriented KPIs that reflect safety, reliability, and responsible behavior of AI systems. Error rate (safety/quality signal) and bias detection (fairness/compliance signal) provide leading indicators of model risk, potential user harm, and regulatory exposure-key interests for a CISO. Explainability and F1 (A) are model performance
/interpretability metrics; customer effort/retention (B) are business CX metrics; response time/throughput (C) are operational SRE metrics. While valuable, they are secondary to risk-centric KPIs for CISO oversight.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Metrics and Assurance; Governance Dashboards for AI. AAISM Study Guide - Operationalizing AI Controls; Safety, Fairness, and Compliance Indicators for Executive Reporting.
O Error rate and bias detection


NEW QUESTION # 426
A school district contracts a third-party provider for AI-based curriculum recommendations. Which of the following is the BEST way to ensure the vendor uses AI responsibly?

Answer: D

Explanation:
AAISM emphasizes transparency artifacts from vendors to enable due diligence and assurance.
A model card documents intended use, data sources, limitations, performance across subgroups, known risks, and evaluation procedures--information necessary to assess safety, fairness, and compliance for sensitive contexts like education. SSO and support are useful operational features; generic ToS updates are insufficient without model-specific disclosures.


NEW QUESTION # 427
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

Answer: A

Explanation:
AAISM identifies robust data validation and anomaly detection on incoming training data as the primary defense against data poisoning. These controls detect corrupted, manipulated, or adversarial samples before they enter the training pipeline.


NEW QUESTION # 428
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