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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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
Which of the following mitigation control strategies would BEST reduce the risk of introducing hidden backdoors during model fine-tuning via third-party components?

A. Leveraging open-source models and packages
B. Performing threat modeling and integrity checks
C. Implementing unsupervised learning methods
D. Disabling runtime logs during model training

Answer: B

Explanation:
AAISM highlights threat modeling and supply chain integrity checks as key controls for managing AI- specific risks, including hidden backdoors in third-party models, libraries, or fine-tuning artifacts. The official guidance states that organizations should "identify adversarial insertion points, verify component integrity, and continuously test for malicious behaviors introduced via external components." This is precisely what option B describes. Simply using open-source (A) does not guarantee security; code can still contain malicious modifications. Disabling runtime logs (C) would reduce visibility, making backdoor detection harder. Choosing unsupervised learning (D) is a modeling approach and has no inherent relation to backdoor risk reduction. The explicit combination of AI-focused threat modeling and integrity verification of external components is the recommended best practice to mitigate this class of attack.
References: AI Security Management™ (AAISM) Study Guide - AI Supply Chain Risk Management; Threat Modeling and Component Integrity.

NEW QUESTION # 161
Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?

A. Implementing an automated data cataloging tool that integrates with all organizational data repositories
B. Conducting quarterly audits of AI data sets for anomalies and missing metadata
C. Creating a centralized team to oversee the classification of data used in AI projects
D. Establishing a manual process to categorize data based on business needs and regulatory compliance

Answer: A

Explanation:
AAISM governance practices emphasize automation as the most effective way to maintain comprehensive, accurate, and scalable data classification and inventory management. An automated data cataloging tool integrated with all repositories ensures continuous visibility, reduces human error, and supports regulatory compliance. Centralized teams and manual processes provide oversight but cannot achieve the same scale and consistency. Periodic audits detect issues but are reactive rather than proactive. For effective governance, the best solution is automated cataloging integrated across data repositories.

NEW QUESTION # 162
Which of the following methods provides the MOST effective protection against model inversion attacks?

A. Using adversarial training
B. Reducing the model's complexity
C. Implementing regularization output
D. Increasing the number of training iterations

Answer: C

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.

NEW QUESTION # 163
An organization is implementing an AI-based credit assessment engine using internal and third- party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?

A. Limitation of model training to structured data from vetted sources to minimize ingestion risk
B. Use of hashed identifiers to anonymize datasets used for model validation and internal analytics
C. Encrypted isolation and dynamic access controls on training data pipelines
D. Documented procedures for data sourcing, lineage tracking, and quality validation

Answer: D

Explanation:
AAISM emphasizes that data governance over the full AI life cycle is foundational. The official content describes effective AI data management as including documented procedures for: (1) how data is sourced, (2) how lineage is tracked from origin to model, and (3) how data quality is validated and monitored. This ensures transparency, accountability, and auditability, which are especially critical in regulated areas like credit assessments.

NEW QUESTION # 164
A pharmaceutical organization uses an AI solution from a third-party vendor to analyze confidential research and development data. Which of the following controls is MOST essential to ensure the vendor's AI solution complies with the organization's security requirements?

A. Review the vendor's independent security audit report.
B. Implement a private instance of the solution.
C. Minimize sensitive data being used by the vendor's AI solution.
D. Communicate security requirements to the vendor.

Answer: A

Explanation:
Reviewing the vendor's independent security audit report provides objective assurance that the vendor's AI solution and related controls have been evaluated against recognized security standards and requirements. This is essential for validating compliance with the organization's security expectations when handling confidential research data.

NEW QUESTION # 165
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