ISACA AAISM Questions - Latest Preparation Material (2026)

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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
AI Governance and Program Management31%- Stakeholder considerations and governance frameworks
  • 1. Industry frameworks and regulatory requirements
  • 2. AI governance models and oversight
- AI policies and procedures
  • 1. Policy implementation and compliance
  • 2. AI-related strategy development
- AI security program management
  • 1. Program development and maintenance
  • 2. Incident response and business continuity
- AI asset and data lifecycle management
  • 1. AI asset inventory and management
  • 2. Data governance
AI Technologies and Controls38%- AI security architecture and design
  • 1. Secure AI system architecture
  • 2. AI infrastructure protection
- Data management and privacy controls
  • 1. Data integrity and protection
  • 2. Privacy and ethical AI controls
- Trust, safety and compliance controls
  • 1. Compliance and governance validation
  • 2. Responsible AI practices
- AI security monitoring and operations
  • 1. Security monitoring and logging
  • 2. Operational AI controls
AI Risk Management31%- Third-party and supply chain risk management
  • 1. Vendor risk evaluation
  • 2. Supply chain security controls
- AI threat and vulnerability management
  • 1. Threat modeling for AI systems
  • 2. AI vulnerability assessment
- AI risk assessment and treatment
  • 1. Risk identification and analysis
  • 2. Risk thresholds and mitigation

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Free PDF 2026 AAISM: ISACA Advanced in AI Security Management (AAISM) Exam –High Pass-Rate Free Brain Dumps

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q387-Q392):

NEW QUESTION # 387
When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?

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 # 388
A financial organization is concerned about AI data poisoning. Which control BEST mitigates this risk?

Answer: A

Explanation:
AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:
* attack samples become harder to inject
* anomalies are easier to detect
* corrupted data has reduced influence on model behavior
Break-glass procedures (A) relate to incident response, not mitigation. Customer transparency (B) does not stop poisoning. Awareness training (D) is insufficient alone.
References: AAISM Study Guide - Data Poisoning Mitigation; Data Diversity and Source Validation.


NEW QUESTION # 389
A critical AI system shows biased outcomes. What is the BEST course of action?

Answer: B

Explanation:
AAISM emphasizes root cause analysis as the primary step when AI behavior deviates unexpectedly. RCA determines whether issues stem from:
* data drift
* model drift
* poisoned data
* missing features
* logic errors
* configuration changes
Retraining (D) may be required later, but only after RCA. Kill switches (A) are last-resort tools. Audits (B) help but are narrower than RCA.
References: AAISM Study Guide - Incident Response for AI; Root Cause First Principle.


NEW QUESTION # 390
A preliminary risk assessment of a SaaS-based large language model (LLM) business support system has identified prompt injection, data poisoning, and model exfiltration as material threats. Which of the following is the BEST approach to ensure risks are treated consistently?

Answer: B

Explanation:
AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Treatment Planning; Threat-Control Traceability; Assurance & Evidence Management for AI Systems.


NEW QUESTION # 391
An organization's HR chatbot was discovered generating fake employee details using a third- party API. Which would be the information security team's BEST recommendation?

Answer: C

Explanation:
The issue stems from the chatbot relying on an external API that is producing unreliable outputs.
Limiting or restricting communication with the third-party API reduces exposure to untrusted responses and prevents further generation of incorrect or fabricated employee information, directly mitigating the risk.


NEW QUESTION # 392
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