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ISACA AAISM Exam Syllabus Topics:

TopicDetails
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 (Q22-Q27):

NEW QUESTION # 22
A data scientist creating categories and training the algorithm on large data sets is an example of which of the following types of AI model learning techniques?

Answer: A

Explanation:
AAISM classifies learning paradigms by the presence of labeled targets. Creating categories (labels) and training on them is supervised learning, where input features are mapped to known outputs and optimization minimizes prediction error against ground truth.


NEW QUESTION # 23
Which of the following should be the PRIMARY consideration for an organization concerned about liabilities associated with unforeseen behavior from agentic AI systems?

Answer: D

Explanation:
AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Accountability and Liability Management) AI Security Management Study Guide - Responsible Oversight of Agentic AI


NEW QUESTION # 24
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?

Answer: B

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.


NEW QUESTION # 25
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

Answer: C

Explanation:
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack--data pipelines, feature stores, model registries, inference services, and dependent infrastructure--can actually fail over and recover within RTO/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.


NEW QUESTION # 26
A healthcare organization plans to use agentic AI for patient scheduling and claims. Management is concerned that the system may act independently and cause compliance or privacy issues.
Which of the following is the BEST way to address this concern?

Answer: A

Explanation:
Conducting an AI impact assessment allows the organization to evaluate privacy, compliance, operational, and autonomy-related risks before deployment. Assessing acceptable levels of autonomy helps ensure the agentic AI system operates within defined governance, legal, and ethical boundaries appropriate for healthcare environments.


NEW QUESTION # 27
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