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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 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 (Q59-Q64):

NEW QUESTION # 59
To facilitate the adoption of a new AI system, an organization has provided data access to third- party solutions. Which of the following BEST mitigates risk associated with third-party software?

Answer: A

Explanation:
Conducting regular external audits provides independent assessment and verification that third- party solutions are operating securely, complying with requirements, and properly handling organizational data. This is the most effective way to mitigate ongoing risks associated with third- party software access and AI integration.


NEW QUESTION # 60
Which of the following controls BEST mitigates the risk of bias in AI models?

Answer: A

Explanation:
Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance.
Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Bias and Fairness Management) AI Security Management Study Guide - Data Governance and Bias Reduction Strategies


NEW QUESTION # 61
Which of the following is the GREATEST benefit of performing AI security risk assessments?

Answer: C

Explanation:
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.


NEW QUESTION # 62
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?

Answer: A

Explanation:
AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.
Output moderation detects:
- maliciously influenced responses
- unsafe outputs
- security-policy violations


NEW QUESTION # 63
Which of the following is the MOST effective and secure way to prevent model attribute attacks against an organization's centralized AI system training data that includes sensitive information about individuals?

Answer: C

Explanation:
Federated learning keeps sensitive data decentralized by training models locally and sharing only model updates rather than raw data. This significantly reduces exposure of individual-level information and helps prevent attribute inference attacks against centralized training datasets.


NEW QUESTION # 64
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