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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Related Certifications:CISM
CISSP
Certificate Validity Period:3 years
Exam Format:Scenario-based, Multiple-choice
Real Exam Qty:90
Exam Duration:150 minutes
Available Languages:English
Exam Price:US$459 (members), US$599 (non-members)
Passing Score:450 (out of 800)
Recommended Training:ISACA Official AAISM Training
Exam Registration:ISACA AAISM Registration
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:Hold an active CISM or CISSP certification
Official Syllabus URL:https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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New AAISM Exam Answers & AAISM Exam Pass4sure

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which of the following would BEST help an organization align its AI initiatives with business objectives?

Answer: A

Explanation:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management™ (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.


NEW QUESTION # 34
What is the GREATEST concern when a vendor enables generative AI features for an organization's critical system?

Answer: B

Explanation:
AAISM highlights that uncontrolled access to generative AI in critical systems introduces the highest level of risk, as such models can:
- expose sensitive data
- execute unintended actions
- be manipulated through injected prompts
- cause operational instability


NEW QUESTION # 35
An attacker is able to exploit the intended behavior of an AI system using a specific input or classes of inputs. Which type of attack is this an example of?

Answer: D

Explanation:
An evasion attack occurs when an attacker crafts specific inputs designed to exploit the AI system during operation, causing the model to produce incorrect or unintended behavior while still functioning within its deployed environment.


NEW QUESTION # 36
Which of the following is the MOST likely cause of model drift?

Answer: C

Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data-centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from.


NEW QUESTION # 37
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

Answer: D

Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.


NEW QUESTION # 38
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