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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 (Q144-Q149):

NEW QUESTION # 144
A security assessment revealed that attackers could access sensitive company data through chat interface injection. What is the BEST mitigation?

A. Implementing input validation and templates
B. Manually reviewing AI model outputs
C. Conducting regular security audits
D. Ensuring continuous monitoring and tagging

Answer: A

Explanation:
AAISM explains that prompt injection attacks are best mitigated by:
- strict input validation
- templated prompts
- controlled context windows
- guardrail enforcement
These prevent malicious instructions from overriding system prompts.

NEW QUESTION # 145
Embedding unique identifiers into AI models would BEST help with:

A. Preventing unauthorized access
B. Tracking ownership
C. Detecting adversarial attacks
D. Eliminating AI system biases

Answer: B

Explanation:
The AAISM framework explains that embedding unique identifiers-such as digital watermarks or model fingerprints-enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Model Provenance and Watermarking) AI Security Management Study Guide - Ownership and Accountability of Models

NEW QUESTION # 146
Which of the following is the MOST important factor to consider when selecting industry frameworks to align organizational AI governance with business objectives?

A. Risk tolerance
B. Risk appetite
C. Risk threshold
D. Risk register

Answer: B

Explanation:
According to AAISM governance principles, the risk appetite of the organization is the most important factor in selecting appropriate frameworks for AI governance. Risk appetite defines the level of risk an organization is willing to accept in pursuit of its objectives, ensuring frameworks are aligned with strategic goals. Risk tolerance and thresholds are operational measures derived from appetite, and the risk register is a documentation tool. The foundational consideration for framework alignment is the organization's risk appetite.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Risk Appetite in Governance Alignment) AI Security Management Study Guide - Framework Selection and Business Strategy

NEW QUESTION # 147
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?

A. Activate filtering logic to exclude intellectual property flags
B. Appoint a data steward specialized in AI to strengthen security governance
C. Disclose service provider policies to declare compliance with regulations
D. Review log information that records how data was collected

Answer: A

Explanation:
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation.
While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.

NEW QUESTION # 148
Which of the following would BEST help mitigate vulnerabilities associated with hidden triggers in generative AI models?

A. Regularly retraining the model using a diverse data set
B. Incorporating adversarial training to expose and neutralize potential triggers
C. Monitoring model outputs and suspicious patterns to detect trigger activations
D. Applying differential privacy and masking sensitive patterns in the training data

Answer: B

Explanation:
Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs.
The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience.
Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.

NEW QUESTION # 149
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