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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Price:	USD 459 (member) / USD 599 (non-member)
Passing Score:	450/800
Related Certifications:	CISSP CISM
Real Exam Qty:	90
Exam Format:	Multiple-choice questions
Available Languages:	English, Spanish
Certificate Validity Period:	3 years (continuing professional education required for renewal)
Exam Duration:	150 minutes
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 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 (Q206-Q211):

NEW QUESTION # 206
Which of the following BEST addresses risk associated with hallucinations in AI systems?

A. Content enrichment
B. Automated output validation
C. Recursive chunking
D. Human oversight

Answer: D

Explanation:
AAISM prescribes human-in-the-loop (HITL) controls as the primary safeguard for high-impact generative AI use cases to mitigate hallucination risk. Human oversight ensures critical outputs are reviewed, corrected, and approved before use, with accountability, escalation, and documented decision trails. Automated validators and enrichment help reduce errors but are secondary; recursive chunking is a prompting tactic, not a governance control.
References: AI Security Management™ (AAISM) Body of Knowledge: Responsible AI & Human Oversight; Generative AI Risk Controls-Approval Workflows and Human Review; AAISM Study Guide: Hallucination Risk Treatment with HITL and Approval Gates.

NEW QUESTION # 207
Which strategy BEST ensures generative AI tools do not expose company data?

A. Testing AI tools before implementation
B. Conducting an independent AI data audit
C. Implementing a solution prohibiting input of sensitive data
D. Ensuring AI tools comply with local regulations

Answer: C

Explanation:
AAISM identifies the strongest immediate control for preventing data leakage into generative AI systems as technically restricting or blocking user entry of sensitive data.
Audits (A) are retrospective. Testing tools (C) does not prevent user error. Regulatory compliance (D) does not stop operational leakage.
References: AAISM Study Guide - Data Leakage Prevention and Input Restriction Controls.

NEW QUESTION # 208
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. Require opt-out provisions for data usage in service agreements
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. Establish policies and awareness training for acceptable use of AI

Answer: A

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. Third-party audit reports (A) provide assurance but do not guarantee provider behavior for your specific data; awareness policies (B) are necessary but insufficient to control external processing; IP ownership guidelines (D) address legal rights, not data-security risk.
References: AI Security Management (AAISM) Body of Knowledge - Third-Party/Procurement Controls; Data Use & Retention Clauses; Training/Fine-tuning Opt-Out; Secure Deletion and Purpose Limitation.

NEW QUESTION # 209
Which of the following should be done FIRST when developing an acceptable use policy for generative AI?

A. Consult with risk management and legal
B. Review AI regulatory requirements
C. Review existing company policies
D. Determine the scope and intended use of AI

Answer: D

Explanation:
According to the AAISM framework, the first step in drafting an acceptable use policy is defining the scope and intended use of the AI system. This ensures that governance, regulatory considerations, risk assessments, and alignment with organizational policies are all tailored to the specific applications and functions the AI will serve. Once scope and intended use are clearly defined, legal, regulatory, and risk considerations can be systematically applied. Without this step, policies risk being generic and misaligned with business objectives.

NEW QUESTION # 210
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data. Which option BEST supports security-by-design?

A. Integrating differential privacy mechanisms into model training
B. Segmenting AI services across containers
C. Applying threat modeling specific to AI components before deployment
D. Restricting access to AI models using IP allow lists

Answer: C

Explanation:
AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by- design, particularly for high-risk systems like credit scoring. It systematically identifies:
- data poisoning
- bias vulnerabilities
- model evasion
- model extraction
- misuse scenarios

NEW QUESTION # 211
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