ISACA AAISM PDF Dumps

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 (Q156-Q161):

NEW QUESTION # 156
When addressing privacy concerns related to AI systems, which of the following is the GREATEST significance of user consent for an organization?

A. It enables users to delete and modify their personal data
B. It prevents unauthorized access to data within the AI system
C. It helps the organization detect biases and ensure fairness
D. It allows the organization to process user data in the AI system

Answer: D

Explanation:
Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits.

NEW QUESTION # 157
Which of the following is the MOST important consideration when an organization is adopting generative AI for personalized advertising?

A. Commercial risk
B. Fraud risk
C. Reputational risk
D. Regulatory risk

Answer: D

Explanation:
In AAISM, usage of AI for activities involving personal data and profiling, such as personalized advertising, is explicitly mapped to stringent regulatory and compliance requirements (e.g., data protection, consent, profiling limitations, fairness obligations). The material notes that these activities may trigger "heightened regulatory scrutiny, mandatory impact assessments, and potential penalties for non-compliance." While reputational (B), fraud (A), and commercial (C) risks are all relevant, the primary, non-optional constraint is compliance with applicable regulations governing personal data, automated profiling, and targeted content.
Failure in this area can lead not only to reputational harm but also to legal sanctions, enforced remediation, and operational restrictions. Therefore, regulatory risk is identified as the most important consideration when deploying generative AI for personalized advertising.
References: AI Security Management™ (AAISM) Study Guide - AI, Privacy, and Regulatory Compliance; High-Risk Use Cases and Profiling.

NEW QUESTION # 158
An organization is facing a deepfake attack intended to manipulate stock prices. The organization's crisis communication plan has been activated. Which of the following is MOST important to include in the initial response?

A. Provide clarifying information in a pre-approved public statement
B. Conduct employee awareness training on recognizing deepfake videos and audio
C. Engage with brand monitoring services to track social media activity
D. Conduct a detailed forensic analysis to identify the source of the deepfake

Answer: A

Explanation:
AAISM guidance on crisis management and communication emphasizes that the initial priority in responding to a reputational or market manipulation attack is to provide accurate clarifying information to the public through a pre-approved statement. This ensures stakeholders and markets are given verified facts immediately, limiting the spread of misinformation. While forensic analysis, employee training, and monitoring activities are important, they occur after the immediate need for public trust and damage control is addressed. Pre-approved statements are a central control in AI-related incident response to ensure consistency, timeliness, and credibility in communications.
References:
AAISM Study Guide - AI Governance and Program Management (Incident Response and Crisis Communication) ISACA AI Security Management - Public Communication and Trust Preservation

NEW QUESTION # 159
Which of the following BEST describes the role of transparency in AI?

A. Talking through a decision tree to better understand how the algorithm made each of its choices
B. Persuading someone that the AI tool in use is beneficial and operates as expected
C. Publishing AI mechanisms, data sources, and decision-making processes while making them openly available
D. Explaining the AI system in an understandable and logical way so reasons for decisions can be given

Answer: D

Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why. Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.

NEW QUESTION # 160
Which of the following would MOST effectively obtain ongoing support from stakeholders to align AI initiatives with business objectives?

A. Addressing and optimizing AI-related risk
B. Developing and monitoring the AI strategic roadmap
C. Conducting periodic organization-wide AI staff training
D. Quantifying and communicating the value of AI solutions

Answer: D

Explanation:
Sustained stakeholder sponsorship hinges on demonstrated, quantified business value communicated in terms they own (KPIs, ROI, cost-to-serve, risk-adjusted outcomes). AAISM frames stakeholder alignment as a value-assurance loop: define value hypotheses, measure realized value, and continuously communicate results to sponsors.

NEW QUESTION # 161
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