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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 (Q83-Q88):

NEW QUESTION # 83
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Ensure backups are tested and access controls are recorded and audited to ensure compliance
B. Ensure the certificate of destruction is received and archived in line with data retention policies
C. Update governance policies based on lessons learned and ensure a feedback loop exists
D. Perform a post-destruction risk assessment to verify that there is no residual exposure of data

Answer: B

Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

NEW QUESTION # 84
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

A. The organization has implemented a responsible development policy
B. Stakeholders have approved alignment with company values
C. Outputs of AI tools do not perpetuate adverse biases
D. AI tools are evaluated by the privacy department before implementation

Answer: C

Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices

NEW QUESTION # 85
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:
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.

NEW QUESTION # 86
Which of the following is MOST important for effective AI risk management?

A. Internal stakeholder participation in AI risk management processes
B. Risk measurement during an early stage of the AI system life cycle
C. Creation of separate risk management processes for AI-specific risk
D. Utilization of best practice AI risk management frameworks

Answer: B

Explanation:
AAISM positions early and continuous risk assessment as a critical success factor. The guidance states that AI risk should be "identified, analyzed, and measured starting from the design and concept phases, before significant investment and deployment." This ensures that high-impact risks (e.g., bias, privacy violations, safety issues) can be mitigated or designed-out before they become embedded in production systems.
Frameworks (A) are valuable, but their effectiveness depends on when and how they are applied. Stakeholder participation (B) is important but is one component of a broader process. Creating completely separate risk processes for AI (D) may fragment governance and is not required; integration with enterprise risk management is preferred. Thus, the timing of risk measurement-early in the life cycle-is identified as the most important factor for effective AI risk management.
References: AI Security Management™ (AAISM) Study Guide - AI Risk Management Life Cycle; Design- Time Risk Identification.

NEW QUESTION # 87
When deriving statistical information from AI systems, which source of risk is MOST important to address?

A. Lack of data normalization
B. Incomplete outputs
C. Systemic bias in data sets
D. Presence of hallucinations

Answer: C

Explanation:
AAISM emphasizes systemic or structural bias as a high-impact risk because biased data leads directly to discriminatory insights or decisions when used for analytics or reporting. This risk affects fairness, compliance, and organizational reputation.
Hallucinations (A) relate more to generative AI. Incomplete outputs (B) affect accuracy but not structural fairness. Lack of normalization (C) affects performance but is not the dominant risk.
References: AAISM Study Guide - AI Bias, Fairness, and Ethical Risk.

NEW QUESTION # 88
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