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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 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 3	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.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q342-Q347):

NEW QUESTION # 342
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

A. Using robust data validation techniques and anomaly detection
B. Increasing model complexity
C. Incorporating more features and data into model training
D. Ensuring the model is trained on diverse data sources

Answer: A

Explanation:
AAISM identifies robust data validation and anomaly detection on incoming training data as the primary defense against data poisoning. These controls detect corrupted, manipulated, or adversarial samples before they enter the training pipeline.
Diverse data (A) is helpful but not protective against poisoning. More complexity (B) does not mitigate poisoning and can worsen vulnerability. More features (D) increases attack surface.
References: AAISM Study Guide - AI Threats; Data Poisoning Mitigation through Validation & Anomaly Detection.

NEW QUESTION # 343
Which of the following would BEST help mitigate vulnerabilities associated with hidden triggers in generative AI models?

A. Regularly retraining the model using a diverse data set
B. Incorporating adversarial training to expose and neutralize potential triggers
C. Monitoring model outputs and suspicious patterns to detect trigger activations
D. Applying differential privacy and masking sensitive patterns in the training data

Answer: B

Explanation:
Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.
References:
AAISM Exam Content Outline - AI Risk Management (Backdoors and Hidden Triggers) AI Security Management Study Guide - Adversarial Training as a Mitigation Control

NEW QUESTION # 344
Which of the following is the BEST technique for addressing the likelihood of a model evasion attack?

A. Establishing continuous monitoring
B. Integrating multiple detection mechanisms
C. Developing a catalog of AI-specific attack scenarios
D. Implementing hashing techniques

Answer: B

Explanation:
Integrating multiple detection mechanisms creates layered defenses, making it significantly harder for adversarial inputs to bypass all controls. This diversity increases resilience against evasion attempts by detecting anomalies through different methods.

NEW QUESTION # 345
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs. Which of the following biases does this scenario demonstrate?

A. Reporting
B. Selection
C. Automation
D. Confirmation

Answer: C

Explanation:
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation. Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
References:
AAISM Exam Content Outline - AI Risk Management (Bias Types in AI)
AI Security Management Study Guide - Automation Bias in AI Use

NEW QUESTION # 346
A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications. Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision-making and prevent bias?

A. Train the system to provide advisory outputs with final decisions made by human experts.
B. Integrate a mechanism for customers to appeal decisions directly within the system.
C. Restrict the model's decision-making criteria to objective financial metrics only.
D. Regularly update the model with new customer data to improve prediction accuracy.

Answer: A

Explanation:
In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.
The AI Security ManagementTM (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the-loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.

NEW QUESTION # 347
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