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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?

A. Implementing a secondary AI system to confirm images
B. Monitoring the system for misuse cases
C. Fine-tuning the AI model to decrease hallucinations
D. Enhancing training data to increase variance

Answer: D

Explanation:
AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts. Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.

NEW QUESTION # 63
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?

A. Train the system to provide advisory outputs with final decisions made by human experts.
B. Integrate a mechanism for customers to appeal decisions directly within the system.
C. Restrict the model's decision-making criteria to objective financial metrics only.
D. Regularly update the model with new customer data to improve prediction accuracy.

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 # 64
Which approach should an organization prioritize to effectively verify the security of its AI models?

A. Automating vulnerability identification
B. Testing team competencies in IT threat mitigation
C. Developing a testing strategy including AI-specific threat modeling and adversarial attack simulations
D. Using standard penetration testing methods

Answer: C

Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems. Effective AI security testing requires:
- AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
- Adversarial attack simulations (white-box, black-box, gradient-based attacks)
- Evaluation of robustness and manipulation resistance

NEW QUESTION # 65
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

A. Absence of metrics and dashboards for analysts
B. Excessive reliance on external consultants for model design
C. Lack of sufficient computing resources for the AI system
D. Insufficient model validation and change control processes

Answer: D

Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
- AI model validation
- Change management
- Governance oversight

NEW QUESTION # 66
The PRIMARY benefit of implementing moderation controls in generative AI applications is that it can:

A. Ensure the generated content adheres to privacy regulations
B. Optimize the model's response time
C. Filter out harmful or inappropriate content
D. Increase the model's ability to generate diverse and creative content

Answer: C

Explanation:
AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users.
This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards. Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.

NEW QUESTION # 67
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