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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Certificate Validity Period:3 years
Passing Score:450/800
Available Languages:English, Spanish
Real Exam Qty:90
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Exam Format:Scenario-based Questions, Multiple Choice
Exam Duration:150 minutes
Exam Price:USD $399
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Online remote proctored exam or test center exam
Pre Condition:Candidates must hold an active CISM or CISSP certification.
Official Syllabus URL:https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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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 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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q313-Q318):

NEW QUESTION # 313
Implementing which of the following would MOST effectively address bias in generative AI models?

Answer: C

Explanation:
AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.
References: AI Security Management (AAISM) Body of Knowledge - Fairness & Bias Management in Generative AI; Metrics, Constraints, and Remediation. AAISM Study Guide - Fairness Objectives, Post-hoc Debiasing, and Evaluation Protocols.


NEW QUESTION # 314
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:

Answer: D

Explanation:
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
References:
AAISM Study Guide - AI Governance and Program Management (Ethical AI and Accountability) ISACA AI Security Management - Responsible AI Practices


NEW QUESTION # 315
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?

Answer: C

Explanation:
AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:
* model behavior
* compliance gaps
* security improvements
* governance alignment
Internal auditors (B) perform assessments but do not implement changes. System architects (A) support technical fixes but do not own compliance. End users (C) are not responsible for audit remediation.
References: AAISM Study Guide - Roles & Responsibilities; AI Ownership and Accountability.


NEW QUESTION # 316
When an attacker uses synthetic data to reverse engineer an organization's AI model, it is an example of which of the following types of attack?

Answer: D

Explanation:
Explanation:
Model distillation attacks involve using synthetic or carefully crafted data to query an AI model repeatedly and reconstruct its underlying parameters or behavior. This allows attackers to approximate the original model without direct access, effectively reverse engineering it.


NEW QUESTION # 317
Which of the following BEST describes an adversarial attack on an AI model?

Answer: B

Explanation:
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged. Hardware compromise (A) and DoS (D) are system-level attacks, not adversarial ML per se. Social engineering to obtain model information (C) is an information-gathering or security breach vector, distinct from input-space adversarial manipulation.
References:AI Security Management (AAISM) Body of Knowledge: Taxonomy of AI Threats- Adversarial Examples and Evasion; Distinction from System/Network Attacks.AAISM Study Guide:
Definitions and Threat Models in Adversarial ML; Attack-Defense Mapping for Model Predictions.


NEW QUESTION # 318
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