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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 (Q173-Q178):

NEW QUESTION # 173
AI developers often find it difficult to explain the processes inside deep learning systems PRIMARILY because:

A. Applied algorithms are based on probability theories to improve system performance
B. Neural network architectures can include statistical methods that are not fully understood
C. Training data input for learning is spread throughout the public domain and continues to change
D. Generated knowledge dynamically changes in memory without being tracked by change history logs

Answer: B

Explanation:
Deep learning models learn high-dimensional, non-linear representations through layered parameterization that resists simple causal narratives. The internal mechanisms (e.g., distributed feature representations and complex statistical transformations) are difficult to map to human- interpretable rules, making explanation challenging. This is the primary reason for explainability difficulty in deep learning.

NEW QUESTION # 174
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?

A. Unauthorized tuning
B. Data poisoning
C. Model inversion
D. Model distillation

Answer: C

Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces

NEW QUESTION # 175
Which of the following controls BEST mitigates the inherent limitations of generative AI models?

A. Reverse engineering the models
B. Adopting AI-specific regulations
C. Classifying and labeling AI systems
D. Ensuring human oversight

Answer: D

Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-- including hallucinations, bias, and unpredictability--are best mitigated by human oversight.
Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability.
Governance best practices highlight human oversight as the critical safeguard.

NEW QUESTION # 176
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Update governance policies based on lessons learned and ensure a feedback loop exists
B. Ensure backups are tested and access controls are recorded and audited to ensure compliance
C. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
D. Ensure the certificate of destruction is received and archived in line with data retention policies

Answer: D

Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

NEW QUESTION # 177
Which of the following MOST effectively addresses bias in generative AI models?

A. Adversarial training
B. Data minimization
C. Data augmentation
D. Fairness constraints

Answer: D

Explanation:
AAISM identifies fairness constraints as a direct mechanism to mitigate and control model bias by embedding fairness requirements into optimization objectives during training.
Data augmentation (B) helps but is not a primary anti-bias control. Adversarial training (C) focuses on robustness, not fairness. Minimization (A) reduces data, often making bias worse.
References: AAISM Study Guide - Fairness, Bias Mitigation Techniques, Ethical AI Controls.

NEW QUESTION # 178
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