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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

Answer: D

Explanation:
The priority control in AI vendor due diligence is ensuring explicit disclosure of data handling: data flows, purpose limitation, retention/deletion, training vs. inference use, isolation controls, access paths, subcontractors, and storage/transfer boundaries. This disclosure is then tied to contractual commitments and measurable controls. A public policy (Option A) may be incomplete; a pre-procurement external audit (Option C) can be valuable but is not always feasible or targeted to your data use; legal discussions (Option D) are necessary for terms but must be grounded in clear, detailed data-use disclosures to be effective.
References:
AAISM Body of Knowledge: Third-Party AI Risk Management; Data Governance and Usage Controls; Contractual and Technical Safeguards for Vendor AI.
AAISM Study Guide: AI Procurement Due Diligence; Data-Use Transparency (Training vs. Fine-tuning vs.
Inference); Retention, Purpose Limitation, and Cross-Border Controls.


NEW QUESTION # 60
A financial organization is concerned about the risk of prompt injection attacks on its customer service chatbot. Which of the following controls BEST addresses this concern?

Answer: D

Explanation:
AAISM emphasizes preventive technical controls for LLM threats such as prompt injection, including input validation/sanitization, instruction isolation, allow/deny lists, context segmentation, and output filtering.
These reduce the model's exposure to adversarial instructions embedded in user prompts or retrieved context.
Monitoring (A) is detective, not preventive; increasing parameters (B) does not inherently improve security against injection; human-in-the-loop (D) is valuable for high-risk decisions but does not directly neutralize injection vectors at the control boundary the way input validation and content filtering do.
References: AI Security Management (AAISM) Body of Knowledge - Technical Controls for LLM Security; Input/Output Filtering and Context Isolation; Secure Inference and Prompt Injection Mitigations.


NEW QUESTION # 61
Which of the following should be the MOST important consideration when conducting an AI impact assessment?

Answer: B

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 # 62
Which of the following is MOST important to ensure security throughout the AI data life cycle?

Answer: A

Explanation:
AAISM emphasizes data lineage, provenance tracking, and inventory completeness as essential controls to ensure data security and accountability across all AI data life-cycle phases. This enables detection of unauthorized modifications, improper use, and compliance violations.
Periodic reviews (B) are necessary but insufficient without lineage. Restricting third-party use (C) is one control but not comprehensive. Open-source model choice (A) does not secure data.
References: AAISM Study Guide - AI Data Governance; Lineage and Traceability.


NEW QUESTION # 63
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

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 # 64
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