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Exam AAISM Objectives Pdf | Test AAISM King

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which BEST describes the role of model cards in AI solutions?

A. They automatically fine-tune AI models
B. They help developers create synthetic data
C. They document training data and AI model use cases
D. They visualize AI model performance

Answer: C

Explanation:
AAISM explains that model cards provide structured documentation about AI models, including:
* intended use cases
* training data characteristics
* ethical considerations
* known limitations
* risk factors
* performance benchmarks
They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).
References: AAISM Study Guide - AI Transparency & Model Cards.

NEW QUESTION # 66
An organization is deploying a large language model (LLM) and is concerned that input manipulations may compromise its integrity. Which of the following is the MOST effective way to determine an acceptable risk threshold?

A. Assess the business impact of known threats
B. Deploy a real-time logging and monitoring system
C. Implement a static risk threshold by limiting LLM outputs
D. Restrict all user inputs containing special characters

Answer: A

Explanation:
AAISM requires that risk thresholds/tolerances be set by aligning threat likelihood and impact with the organization's business context and risk appetite. Determining "acceptable" risk starts with assessing business impact of credible threats (e.g., prompt injection leading to data exfiltration, policy evasion, or harmful actions), then translating this into control intensity and thresholds. Hard input restrictions (A) and static output caps (C) are blunt measures that may degrade utility without ensuring alignment to risk appetite.
Monitoring (B) is essential for detection, but it does not, by itself, define what level of risk is acceptable.
References: AI Security Management (AAISM) Body of Knowledge - Risk Appetite and Tolerance for AI; Threat Modeling for LLMs; Business Impact Analysis and Risk Acceptance Criteria.

NEW QUESTION # 67
Which of the following is MOST important for effective AI risk management?

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

Answer: C

Explanation:
AAISM emphasizes that effective AI risk management is socio-technical and hinges on cross-functional stakeholder participation (business owners, security, privacy, legal, compliance, data science, operations).
Participation ensures risks are identified, prioritized, and treated in alignment with business context and risk appetite. While early measurement (B) and using frameworks (D) are important, and separate processes (A) can create silos, stakeholder participation is the primary success factor ensuring governance is practical, adopted, and continuously improved.
References: AI Security Management (AAISM) Body of Knowledge - Governance Roles & Accountability; Integrated Risk Management for AI; Cross-Functional Risk Committees and RACI for AI Programs.

NEW QUESTION # 68
When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?

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

Answer: D

Explanation:
The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data.
According to the AI Security Management™ (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased-reflecting skewed representation, sampling imbalance, or embedded prejudice- the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.
Why Option A is Correct:
* Systemic bias affects the integrity and trustworthiness of AI-generated statistical information.
* It can introduce discriminatory outcomes, ethical breaches, and regulatory non-compliance-key concerns in AAISM's AI Risk Management and Governance principles.
* Mitigating systemic bias requires data quality assessments, fairness audits, bias detection tools, and model interpretability measures to ensure the derived insights are accurate and ethically sound.
Why Other Options Are Incorrect:
* Option B: Incomplete outputs can affect accuracy but are typically handled through process monitoring or retraining, not as a primary risk factor in statistical validity.
* Option C: Lack of data normalization is a technical preprocessing issue, not a governance-level risk impacting statistical trustworthiness.
* Option D: Hallucinations occur mainly in generative models (e.g., LLMs) and affect content generation, not statistical computation pipelines.
Exact Extract from Official AAISM Study Guide:
"Systemic bias in AI training and inference data represents the most material statistical risk. Bias propagates through derived metrics, predictive models, and decision outputs, compromising fairness, accuracy, and compliance. AI Security Management requires implementing bias detection, fairness testing, and governance mechanisms to identify and mitigate such systemic bias before using AI-generated analytics for organizational or regulatory reporting." References:
AI Security Management™ (AAISM) Body of Knowledge: AI Risk Identification and Evaluation, Bias and Fairness Management in AI Systems.
AI Security Management™ Study Guide: Systemic Bias Mitigation Techniques, Fairness Assurance in AI Analytics.
ISO/IEC 23894:2023 - Clause 7.2: Bias identification and treatment within AI risk frameworks.

NEW QUESTION # 69
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which of the following types of attacks is this an example of?

A. Remote code execution
B. Jailbreaking
C. Prompt injection
D. Evasion

Answer: C

Explanation:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Prompt Security and Input Manipulation) AI Security Management Study Guide - Threats to Output Integrity

NEW QUESTION # 70
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