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ISACA AAISM Exam Syllabus Topics:

Topic	Details
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 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 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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following is the MOST likely cause of model drift?

A. Model stealing
B. Perfect knowledge
C. Membership inference
D. Data poisoning

Answer: D

Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback

NEW QUESTION # 64
Which of the following employee awareness topics would MOST likely be revised to account for AI-enabled cyber risk?

A. Social engineering
B. Clean desk policy
C. Malicious insider threats
D. Authentication controls

Answer: A

Explanation:
AAISM training guidance specifies that social engineering is the awareness topic most impacted by AI-enabled risks. With generative AI and deepfake technologies, attackers can create highly convincing phishing messages, synthetic voices, or fake executive requests, increasing the sophistication of social engineering attacks. Clean desk policies, insider threat awareness, and authentication procedures remain relevant but are not directly altered by AI advancements. The most likely revision to employee awareness programs in the AI era is therefore enhanced social engineering awareness.

NEW QUESTION # 65
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?

A. Explainable decision-making
B. Model validation
C. Control self-assessment (CSA)
D. Key performance indicator (KPI)

Answer: D

Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy. Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.

NEW QUESTION # 66
An organization is designing an AI-based credit risk assessment system that will integrate with sensitive financial datasets. Which of the following would BEST support the implementation of security-by-design principles in the AI system's architecture?

A. Applying threat modeling specific to AI components before deployment
B. Segmenting AI services across containers to manage resource constraints
C. Restricting access to AI models using IP allow lists to reduce public exposure
D. Integrating differential privacy mechanisms into model training to limit data leakage

Answer: A

Explanation:
Security by design in AI requires establishing risk-informed requirements at the earliest stages of the lifecycle and systematically translating them into architectural controls. Conducting AI-specific threat modeling before deployment is the highest-leverage action because it identifies assets (data, models, pipelines), trust boundaries (feature stores, training/inference services), threat events (poisoning, evasion, model extraction), and attack paths unique to ML systems. The outputs (abuse/misuse cases, control objectives, verification plans) then drive selection and prioritization of controls such as privacy-enhancing techniques, access controls, isolation, monitoring, and assurance testing. While differential privacy (C) is a strong control for leakage risk, it is one control choice among many and should be selected as a result of threat modeling. IP allow lists (B) and container segmentation (A) are valuable hardening measures but are narrower and do not replace the lifecycle-wide governance and design traceability that threat modeling enables.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure AI SDLC; AI Threat Modeling and Abuse Case Development; Architecture & Control Selection; Risk-Based Design Assurance.
AAISM Study Guide - Security-by-Design for AI; Model/System Asset Mapping; Control Objectives from Threat Models.

NEW QUESTION # 67
An organization uses a vendor's AI service to screen job applicants. The master service agreement includes a clause related to accountability models. Which of the following is the PRIMARY purpose of this clause in managing AI-specific risk?

A. To provide the company with a full copy of the vendor's source code for independent auditing
B. To ensure the vendor's AI model is built using a specific pre-approved framework
C. To explicitly define the responsibilities of the involved parties
D. To establish the financial model for how the company will pay for the vendor's API usage

Answer: C

Explanation:
Accountability clauses in AI service agreements are primarily intended to clearly define the responsibilities, obligations, and liabilities of each party involved in the use, operation, governance, and oversight of the AI system. This helps manage AI-specific risks related to compliance, security, ethics, and operational failures.

NEW QUESTION # 68
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