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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Real Exam Qty:	90
Exam Duration:	150 minutes
Certificate Validity Period:	3 years (continuing professional education required for renewal)
Passing Score:	450/800
Exam Price:	USD 459 (member) / USD 599 (non-member)
Available Languages:	Spanish, English
Related Certifications:	CISM CISSP
Exam Format:	Multiple-choice questions
Recommended Training:	ISACA Official Training & Review Materials
Exam Registration:	ISACA AAISM Credentialing Page
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based exam (online proctored or authorized test center)
Pre Condition:	Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism

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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 (Q309-Q314):

NEW QUESTION # 309
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. Establish guidelines and best practices with third parties for intellectual property ownership
C. Rely on the AI provider's independent third-party audit reports for assurance
D. Require opt-out provisions for data usage in service agreements

Answer: D

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 # 310
The PRIMARY benefit of implementing moderation controls in generative AI applications is that it can:

A. Ensure the generated content adheres to privacy regulations
B. Optimize the model's response time
C. Increase the model's ability to generate diverse and creative content
D. Filter out harmful or inappropriate content

Answer: D

Explanation:
AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users.
This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards. Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.

NEW QUESTION # 311
The BEST reason for inviting independent reviewers to evaluate the output of an AI application is to:

A. improve accountability.
B. mitigate internal bias.
C. enhance governance.
D. improve transparency.

Answer: B

Explanation:
Independent reviewers provide an external perspective that helps identify and reduce biases that internal teams may overlook due to familiarity or shared assumptions, leading to more objective evaluation of AI outputs.

NEW QUESTION # 312
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?

A. The amount of data in the model
B. The accuracy rate of the model
C. The rate of drift in the model
D. The response time of the model

Answer: C

Explanation:
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.
References:
AAISM Study Guide - AI Risk Management (Monitoring and Drift Detection) ISACA AI Security Management - Key Risk Indicators for AI Systems

NEW QUESTION # 313
Which of the following MOST effectively secures ongoing stakeholder support for AI initiatives?

A. Quantifying and communicating the value of AI solutions
B. Developing and monitoring an AI strategic roadmap
C. Addressing and optimizing AI-related risk
D. Conducting periodic staff training

Answer: A

Explanation:
AAISM governance guidance emphasizes that stakeholder buy-in is sustained when the measurable value of AI initiatives is clearly communicated. Value demonstrations include:
- improved efficiency
- reduced cost
- reduced risk
- business growth

NEW QUESTION # 314
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