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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 Price:	US$459 (members), US$599 (non-members)
Available Languages:	English
Exam Format:	Scenario-based, Multiple-choice
Passing Score:	450 (out of 800)
Exam Duration:	150 minutes
Real Exam Qty:	90
Certificate Validity Period:	3 years
Related Certifications:	CISSP CISM
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 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 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 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 (Q270-Q275):

NEW QUESTION # 270
The PRIMARY objective of performing tests to detect membership inference in machine learning (ML) models is to determine whether the model:

A. does not exhibit any biases toward certain groups of data.
B. can reveal details about the data it was trained on.
C. can handle changes in the input data without affecting its accuracy.
D. is susceptible to attacks that alter predictions through adversarial inputs.

Answer: B

Explanation:
Membership inference testing evaluates whether a model can disclose whether specific data points were part of its training dataset, indicating potential leakage of sensitive information about the data it was trained on.

NEW QUESTION # 271
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. Rely on the AI provider's independent third-party audit reports for assurance
B. Establish policies and awareness training for acceptable use of AI
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 # 272
An organization lacks visibility into the decision-making processes of a newly deployed AI model, and there are concerns regarding accuracy, bias, and stakeholder trust. Which of the following is the BEST way to improve model accuracy?

A. Increase the size of training datasets to encompass as many various scenarios as possible.
B. Require explainability features and review of AI outputs as part of model governance.
C. Implement a governance system that defines procedures for model testing and validation.
D. Establish monitoring controls to track AI decision-making and escalate anomalies for further analysis.

Answer: A

Explanation:
Expanding the training dataset with diverse and representative scenarios improves the model's ability to generalize and reduces errors, directly enhancing accuracy.

NEW QUESTION # 273
A global financial organization wants to deploy an AI solution for credit scoring. Which of the following should the organization do FIRST?

A. Maintain thorough documentation of the validation process, data used, and results.
B. Perform bias assessments based on cultural differences and demographic landscapes.
C. Verify that the AI solution meets compliance requirements for local laws and regulations.
D. Create a test, evaluation, verification and validation (TEVV) strategy for AI solutions.

Answer: C

Explanation:
Ensuring the solution complies with applicable laws and regulations is the foundational step before any deployment activities. Regulatory compliance defines permissible data use, model behavior, and governance requirements, which must be established before conducting assessments, testing strategies, or documentation.

NEW QUESTION # 274
After deployment, an AI model's output begins to drift outside of the expected range. Which of the following is the development team's BEST course of action?

A. Take the AI model offline
B. Adjust the hyperparameters of the AI model
C. Create an emergency change request to correct the issue
D. Return to an earlier phase in the AI life cycle

Answer: D

Explanation:
AAISM emphasizes that when model drift occurs, the best response is not a quick fix but rather to revisit an earlier phase of the AI life cycle to address data quality, retraining, or evaluation processes. Simply taking the model offline halts functionality without resolution, while adjusting hyperparameters or issuing emergency changes treats the symptom rather than the root cause.
Proper governance requires returning to the design or training phases to re-establish stability, accuracy, and compliance of the model. Thus, the correct approach is to return to an earlier AI lifecycle phase.

NEW QUESTION # 275
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