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New AAISM Test Experience, AAISM Exam Vce

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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 (Q10-Q15):

NEW QUESTION # 10
Which of the following information is MOST important to include in a centralized AI inventory?

A. Foundation model and package registry
B. AI model use cases
C. Training data sets
D. Ownership and accountability of AI systems

Answer: D

Explanation:
AAISM governance practices identify ownership and accountability as the most critical element in any centralized AI inventory. An AI inventory provides oversight by cataloging all AI assets within an organization, and assigning responsibility ensures that each system has clear governance, monitoring, and compliance coverage. While use cases, training data, and registries are valuable metadata, they do not guarantee accountability. Without defined ownership, no party is responsible for addressing risk, bias, or incidents. Therefore, the most important information to include is ownership and accountability details for each AI system.
References:
AAISM Exam Content Outline - AI Governance and Program Management (AI Inventories and Oversight) AI Security Management Study Guide - Ownership and Accountability Structures

NEW QUESTION # 11
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

A. Lack of sufficient computing resources for the AI system
B. Insufficient model validation and change control processes
C. Excessive reliance on external consultants for model design
D. Absence of metrics and dashboard for analysts

Answer: B

Explanation:
AAISM requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management™ (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.

NEW QUESTION # 12
Which of the following is the MOST effective way to identify and address security risk in an AI model?

A. Add more data to the model to increase its accuracy and reduce errors
B. Assign staff to review AI model outputs for accuracy
C. Encrypt the training data and model parameters to prevent unauthorized access
D. Conduct threat modeling to identify vulnerabilities and possible attack methods

Answer: D

Explanation:
AI/ML threat modeling is the most effective structured method to both identify and address model security risks. It systematically surfaces attack classes (poisoning, evasion, membership inference, model extraction, inversion), maps system-specific attack surfaces (data pipelines, feature stores, training artifacts, inference APIs), and drives prioritized mitigations (ingestion validation, robust training, rate-limiting, watermarking, differential privacy, monitoring, red teaming). Output spot-checking (A) finds errors but not security vulnerabilities; encryption (C) protects confidentiality but does not reveal threats or mitigate inference-time attacks; adding data (D) may improve accuracy but does not target adversarial risk.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Identification & Threat Modeling; Attack Surface Analysis for ML; Risk Treatment Planning. AAISM Study Guide - Evasion
/Poisoning/Extraction Controls; Mapping Risks to Controls; Validation and Assurance Activities.

NEW QUESTION # 13
When addressing privacy concerns related to AI systems, which of the following is the GREATEST significance of user consent for an organization?

A. It prevents unauthorized access to data within the AI system
B. It helps the organization detect biases and ensure fairness
C. It allows the organization to process user data in the AI system
D. It enables users to delete and modify their personal data

Answer: C

Explanation:
Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits. While fairness (A) and security controls (C) are essential, they are distinct obligations; data subject rights such as rectification/erasure (B) exist regardless of consent and are not "enabled" by it. Therefore, the greatest significance of consent is that it legally permits processing under declared purposes and constraints.
References: AI Security Management™ (AAISM) Body of Knowledge - Privacy & Data Protection in AI; Lawful Basis and Purpose Limitation. AAISM Study Guide - Consent, Transparency, and Data Subject Rights; Privacy-by-Design Controls in AI Pipelines.

NEW QUESTION # 14
Which of the following controls would BEST help to prevent data poisoning in AI models?

A. Establishing continuous monitoring
B. Implementing a strict data validation mechanism
C. Regularly updating the foundational model
D. Increasing the size of the training data set

Answer: B

Explanation:
The most direct preventative control against data poisoning is robust data validation/ingestion gating:
provenance checks, schema and constraint validation, anomaly/outlier screening, label consistency tests, and whitelist/blacklist source controls before data reaches training pipelines. Larger datasets (A) don't inherently prevent poisoning; monitoring (C) is detective; updating a foundation model (D) does not address tainted inputs entering the pipeline.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats and Training-Time Attacks; Secure Data Ingestion and Validation Controls. AAISM Study Guide - Poisoning Prevention: Provenance, Validation, and Sanitization Gates.

NEW QUESTION # 15
......

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Lee Adams Lee Adams

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