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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q143-Q148):

NEW QUESTION # 143
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?

Answer: C

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. While hashing identifiers (B) and encryption/access controls (C) are important privacy and security mechanisms, they are partial controls within a broader governance framework and do not, on their own, establish end-to-end life-cycle management. Limiting training to structured data (D) is a design choice and may reduce risk but is neither sufficient nor required as a best practice. Option A directly reflects AAISM' s prescribed governance controls for AI data throughout its life cycle.
References: AI Security Management™ (AAISM) Study Guide - AI Data Governance and Life Cycle Management; Data Lineage and Quality Assurance.


NEW QUESTION # 144
Which of the following should be the MOST important consideration when conducting an AI impact assessment?

Answer: C

Explanation:
AAISM defines an AI Impact Assessment (AIIA) as a structured evaluation designed to determine whether an AI system can safely and responsibly support business objectives without creating unacceptable risks.
The framework states that alignment to business objectives is the central purpose of AI adoption and therefore must be the starting point in an impact assessment.
Reputation (D) is a consideration, but it is a secondary outcome of failing to meet objectives responsibly.
Employee retention (B) and training (C) are not core drivers of an AIIA.
References: AAISM Study Guide - AI Impact Assessment; Business Alignment Principles.


NEW QUESTION # 145
An organization is commissioning a third-party AI system using sensitive data. Which metric is MOST important to consider?

Answer: A

Explanation:
AAISM specifies that when AI systems process sensitive or high-impact data (e.g., financial, identity, health), accuracy thresholds become critical risk indicators. Inaccurate predictions can cause harm, regulatory non- compliance, discrimination, or financial loss.
Availability (D) is important but secondary to correctness in sensitive decision scenarios. Response time (B) and accessibility ratings (A) do not outweigh the need for accurate, reliable predictions.
References: AAISM Study Guide - AI Model Risk Metrics; Accuracy Requirements for High-Impact Systems.


NEW QUESTION # 146
When evaluating a third-party AI service provider, which of the following master services agreement provisions is MOST critical for managing security risk?

Answer: B

Explanation:
The most material contractual control for reducing security and privacy risk in outsourced AI services is a data-use restriction that prohibits the provider from using customer data for model training (and from derivative model improvements) unless explicitly authorized. This prevents unintended secondary processing, model inversion exposure of proprietary data, unauthorized profiling, and downstream data proliferation across multi-tenant systems. AAISM positions third-party risk controls to prioritize data minimization, purpose limitation, confidentiality, and downstream controls; among common MSA provisions, data-use limitations directly constrain the provider's technical and organizational handling of sensitive inputs, making it the highest-impact risk-reducing clause. Query throttling (B) and logging (C) are useful operational controls but are secondary to legal/processing authority. Unlimited retraining (D) increases attack surface and cost without addressing the core risk of misuse of customer data.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party & Supply-Chain Governance; Contractual Controls for AI Services; Data Minimization and Purpose Limitation. AAISM Study Guide - Procurement & MSA/DPA Clauses for AI; Provider Model Training and Data-Use Restrictions; Privacy & Confidentiality Safeguards in Outsourced AI.


NEW QUESTION # 147
Within an incident handling process, which of the following would BEST help restore end user trust with an AI system?

Answer: D

Explanation:
Restoring end user trust during incident handling requires visible, immediate assurance that system outcomes are safe and appropriate. AAISM prescribes human oversight and approval gates for high-risk AI decisions, with human validation of outputs before use as a primary control to maintain trust while technical remediation is underway. Prioritization (A) and monitoring (B) aid operations but do not directly rebuild user confidence in outcomes. Post-incident improvements (D) are essential for long-term assurance but do not provide the immediate trust restoration that supervised, human-validated outputs deliver.
References: AI Security Management (AAISM) Body of Knowledge - Incident Handling & Communications; Human Oversight and Approval Gates; Trust Restoration During AI Incidents.


NEW QUESTION # 148
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