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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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 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 3
  • 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.

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Test-Taking Questions AAISM Pre-assessment Test

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q397-Q402):

NEW QUESTION # 397
Which of the following methods provides the MOST effective protection against model inversion attacks?

Answer: D

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.


NEW QUESTION # 398
Which of the following employee awareness topics would MOST likely be revised to account for AI-enabled cyber risk?

Answer: B

Explanation:
AAISM training guidance specifies that social engineering is the awareness topic most impacted by AI- enabled risks. With generative AI and deepfake technologies, attackers can create highly convincing phishing messages, synthetic voices, or fake executive requests, increasing the sophistication of social engineering attacks. Clean desk policies, insider threat awareness, and authentication procedures remain relevant but are not directly altered by AI advancements. The most likely revision to employee awareness programs in the AI era is therefore enhanced social engineering awareness.
References:
AAISM Exam Content Outline - AI Risk Management (Human Factors and Awareness) AI Security Management Study Guide - Social Engineering Risks with AI


NEW QUESTION # 399
Which of the following MOST effectively addresses bias in generative AI models?

Answer: B

Explanation:
AAISM identifies fairness constraints as a direct mechanism to mitigate and control model bias by embedding fairness requirements into optimization objectives during training.


NEW QUESTION # 400
An organization develops and implements an AI-based plug-in for users that summarizes their individual emails. Which of the following is the GREATEST risk associated with this application?

Answer: D

Explanation:
According to AAISM risk management guidance, the greatest risk in AI applications handling personal communication data is inadequate parameter controls, which may allow unintended access, manipulation, or leakage of sensitive information. Plug-ins that interact with emails must enforce strict parameter validation and security restrictions to prevent unauthorized or manipulated inputs. While vulnerability scanning, format incompatibility, and API rate limiting are valid concerns, they are secondary. The primary risk is a lack of strong parameter controls that could expose sensitive content.


NEW QUESTION # 401
When designing an AI security architecture, what is the PRIMARY purpose of using adversarial training?

Answer: A


NEW QUESTION # 402
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