ISACA Advanced in AI Security Management (AAISM) Exam Latest Test Cram & AAISM exam study guide & ISACA Advanced in AI Security Management (AAISM) Exam detail study guides

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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Duration:	150 minutes
Exam Price:	US$459 (members), US$599 (non-members)
Real Exam Qty:	90
Passing Score:	450 (out of 800)
Related Certifications:	CISSP CISM
Certificate Validity Period:	3 years
Available Languages:	English
Exam Format:	Scenario-based, Multiple-choice
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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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 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 (Q347-Q352):

NEW QUESTION # 347
An organization uses an AI model to optimize traffic signals and predict congestion. Due to misconfiguration and lack of validation, the system over-prioritizes low-traffic areas, causing gridlock in major cities. Which of the following should be done FIRST to address this issue?

A. Disable the AI system and issue a public statement.
B. Continue running the AI model and deploy a secondary system.
C. Retrain the model in real time using new traffic data.
D. Roll back the AI system to a previous version.

Answer: D

Explanation:
Rolling back to a previously stable version immediately restores known correct behavior and mitigates ongoing impact. This provides a controlled state before further validation or retraining is performed.

NEW QUESTION # 348
Which of the following methods provides the MOST effective protection against model inversion attacks?

A. Implementing regularization output
B. Reducing the model's complexity
C. Using adversarial training
D. Increasing the number of training iterations

Answer: A

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.

NEW QUESTION # 349
Which of the following is the MOST effective action an organization can take to address data security risk when using generative AI features in an application?

A. Establish policies and awareness training for acceptable use of AI
B. Rely on the AI provider's independent third-party audit reports for assurance
C. Require opt-out provisions for data usage in service agreements
D. Establish guidelines and best practices with third parties for intellectual property ownership

Answer: C

Explanation:
AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data-security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization's data for training or secondary purposes and that mandate retention limits and secure deletion.

NEW QUESTION # 350
An organization plans to use an open-source foundational AI model. Which of the following is MOST important for the AI governance committee to consider when approving its use?

A. AI model support
B. Confidential data leakage
C. Employee privacy rights
D. AI model accuracy

Answer: B

Explanation:
AAISM emphasizes that open-source AI models present elevated data leakage risks because internal data may flow into external, uncontrolled repositories or be used for further training.
Governance bodies must prioritize the risk of data exposure, model reuse, data retention uncertainty, and uncontrolled model behavior.

NEW QUESTION # 351
Which of the following AI incidents would be BEST contained via a kill switch?

A. The agent attempts self-replication.
B. A model version update fails.
C. Graphics processing unit utilization decreases.
D. The query takes too long to complete.

Answer: A

Explanation:
A kill switch is designed to immediately stop or disable an AI system when it exhibits dangerous, uncontrolled, or unauthorized behavior. An agent attempting self-replication represents a critical safety and security threat that requires rapid containment through a kill switch mechanism.

NEW QUESTION # 352
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