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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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q261-Q266):

NEW QUESTION # 261
Which of the following is the BEST control for preventing deepfakes?

A. Regular AI risk assessment
B. Output provenance verification
C. AI governance policies
D. System input validation

Answer: B

Explanation:
Output provenance verification (e.g., robust watermarking, cryptographic signing, content credentials, and chain-of-custody attestations) is the primary preventive and detective control to combat deepfakes at scale. It enables receivers and downstream systems to verify that media originates from trusted sources and has not been tampered with. While risk assessments (Option B) and governance policies (Option C) set expectations, they do not technically prevent forged media. Input validation (Option D) does not address media authenticity once generated or received.
References:
AAISM Body of Knowledge: Content Authenticity, Watermarking, and Provenance; Trustworthy AI Outputs and Media Integrity Controls.
AAISM Study Guide: Mitigations for Synthetic Media Risks; Watermark/Signature Verification Pipelines; Content Credentials in Enterprise Controls.

NEW QUESTION # 262
Which of the following is the BEST way to ensure role clarity and staff effectiveness when implementing AI- assisted security monitoring tools?

A. Transition responsibilities to external consultants
B. Increase budgets for AI certifications
C. Delay implementation until more data scientists are hired
D. Update the security program to include cross-functional AI-specific responsibilities

Answer: D

Explanation:
AAISM stresses that AI-enabled security programs require updated governance structures, including defined cross-functional roles across security, data, development, and operations teams.
Role clarity emerges from updated policies, responsibilities, and oversight-not from delaying adoption (A), outsourcing (D), or simply training (B).
References: AAISM Study Guide - AI Governance Structure; Role Definition and Accountability.

NEW QUESTION # 263
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?

A. Segmenting AI services across containers to manage resource constraints
B. Integrating differential privacy mechanisms into model training to limit data leakage
C. Restricting access to AI models using IP allow lists to reduce public exposure
D. Applying threat modeling specific to AI components before deployment

Answer: D

Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing.

NEW QUESTION # 264
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. Restricting access to AI models using IP allow lists
D. Applying threat modeling specific to AI components before deployment

Answer: D

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
Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.
References: AAISM Study Guide - Security-by-Design; AI Threat Modeling.

NEW QUESTION # 265
A CISO has been tasked with providing key performance indicators (KPIs) on the organization's newly launched AI chatbot. Which of the following are the BEST metrics for the CISO to recommend?

A. Error rate and bias detection
B. Response time and throughput
C. Explainability and F1 score
D. Customer effort score and user retention rate

Answer: A

Explanation:
For executive security and governance reporting, AAISM prioritizes risk-and harm-oriented KPIs that reflect safety, reliability, and responsible behavior of AI systems. Error rate (safety/quality signal) and bias detection (fairness/compliance signal) provide leading indicators of model risk, potential user harm, and regulatory exposure--key interests for a CISO.

NEW QUESTION # 266
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