2026 Accurate ISACA AAISM: ISACA Advanced in AI Security Management (AAISM) Exam Best Preparation Materials

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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Available Languages:	English
Real Exam Qty:	90
Exam Price:	US$459 (members), US$599 (non-members)
Certificate Validity Period:	3 years
Exam Format:	Multiple-choice, Scenario-based
Passing Score:	450 (out of 800)
Exam Duration:	150 minutes
Related Certifications:	CISM CISSP
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 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 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 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 (Q389-Q394):

NEW QUESTION # 389
Which of the following BEST reduces the risk of exposing sensitive data through the output of large language models (LLMs) in applications?

A. Enforcing least privilege access
B. Implementing data sanitization techniques
C. Encrypting data in transit and at rest
D. Conducting adversarial testing

Answer: B

Explanation:
AAISM materials make clear that the best safeguard against sensitive information being leaked through the outputs of LLMs is data sanitization. This involves filtering, redacting, or masking sensitive content before the model can use it, thereby preventing unintended disclosure in outputs. Encryption protects confidentiality in storage and transmission but does not stop output leaks. Adversarial testing helps identify vulnerabilities but does not prevent exposure by itself. Least privilege access restricts who can interact with the model but does not sanitize the content of its outputs. The control most directly tied to preventing leakage is implementing data sanitization techniques.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Data Leakage Prevention) AI Security Management Study Guide - Sensitive Data Controls in Generative AI

NEW QUESTION # 390
When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?

A. Restricting query volume thresholds
B. Prohibiting the use of customer data for model training
C. Sharing real-time log information
D. Guaranteeing unlimited model retraining requests

Answer: B

Explanation:
The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third- party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider's technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause.

NEW QUESTION # 391
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?

A. Establishing clear lines of responsibility for AI model outputs
B. Explaining the overall benefit of the system to stakeholders
C. Identifying hallucinations returned by AI models
D. Determining the aggregate risk of the system

Answer: D

Explanation:
When multiple AI models interact in a connected supply chain system, risks can propagate and compound across models. Assessing the overall, aggregate risk - including errors, biases, or failures from individual models - is challenging but critical for ensuring reliable and safe decision- making.

NEW QUESTION # 392
The MOST important consideration when establishing key risk indicators (KRIs) to monitor AI model drift in an operational machine learning (ML) system is adopting KRIs that:

A. measure utilization rates and trends for AI computational resources.
B. identify variations in model accuracy and outputs.
C. track user satisfaction levels with AI-generated outputs.
D. monitor latency and AI infrastructure uptime.

Answer: B

Explanation:
AI model drift occurs when a model's performance or behavior changes over time due to evolving data patterns or environments. KRIs that identify variations in model accuracy and outputs are the most important because they directly indicate whether the model is deviating from expected performance and reliability.

NEW QUESTION # 393
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 IP ownership guidelines with third parties
B. Require opt-out provisions for data usage
C. Establish policies and awareness training for acceptable AI use
D. Rely on the AI provider's independent audit reports

Answer: C

Explanation:
AAISM stresses that the largest and most immediate risk for generative AI is unintentional data leakage by employees, making acceptable-use policies and staff training the most effective short-term risk mitigation.
Users must be instructed not to input sensitive data, understand usage restrictions, and follow governance requirements.
Relying solely on third-party audits (D) is insufficient. IP agreements (A) and opt-out clauses (B) are contractual protections, not operational safeguards.
References: AAISM Study Guide - Generative AI Security Risks; Policy and Awareness as Primary Controls.

NEW QUESTION # 394
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