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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Risk Management31%- AI threat and vulnerability management
  • 1. Threat modeling for AI systems
  • 2. AI vulnerability assessment
- Third-party and supply chain risk management
  • 1. Supply chain security controls
  • 2. Vendor risk evaluation
- AI risk assessment and treatment
  • 1. Risk thresholds and mitigation
  • 2. Risk identification and analysis
Topic 2: AI Governance and Program Management31%- AI security program management
  • 1. Incident response and business continuity
  • 2. Program development and maintenance
- AI policies and procedures
  • 1. AI-related strategy development
  • 2. Policy implementation and compliance
- AI asset and data lifecycle management
  • 1. Data governance
  • 2. AI asset inventory and management
- Stakeholder considerations and governance frameworks
  • 1. Industry frameworks and regulatory requirements
  • 2. AI governance models and oversight
Topic 3: AI Technologies and Controls38%- AI security monitoring and operations
  • 1. Security monitoring and logging
  • 2. Operational AI controls
- AI security architecture and design
  • 1. Secure AI system architecture
  • 2. AI infrastructure protection
- Trust, safety and compliance controls
  • 1. Compliance and governance validation
  • 2. Responsible AI practices
- Data management and privacy controls
  • 1. Data integrity and protection
  • 2. Privacy and ethical AI controls

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q217-Q222):

NEW QUESTION # 217
When preparing for an AI incident, which of the following should be done FIRST?

Answer: B

Explanation:
AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.
References:* AI Security Management™ (AAISM) Body of Knowledge: Incident Management-Preparation; Roles & Responsibilities; Cross-functional Coordination* AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority & Escalation* AAISM Mapping to Standards: Security Operations- Preparation Before Procedures (people and roles precede playbooks)


NEW QUESTION # 218
Which of the following would MOST effectively ensure an organization developing AI systems has comprehensive data classification and inventory management?

Answer: A

Explanation:
AAISM governance practices emphasize automation as the most effective way to maintain comprehensive, accurate, and scalable data classification and inventory management. An automated data cataloging tool integrated with all repositories ensures continuous visibility, reduces human error, and supports regulatory compliance. Centralized teams and manual processes provide oversight but cannot achieve the same scale and consistency. Periodic audits detect issues but are reactive rather than proactive. For effective governance, the best solution is automated cataloging integrated across data repositories.
References:
AAISM Study Guide - AI Governance and Program Management (Data Classification and Inventory) ISACA AI Security Management - Automated Data Cataloging for AI Projects


NEW QUESTION # 219
Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?

Answer: D

Explanation:
AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts. Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.


NEW QUESTION # 220
Which of the following BEST ensures AI components are validated during disaster recovery testing?

Answer: D

Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover. The only option that tests actual operational continuity of AI components is:
monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.


NEW QUESTION # 221
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?

Answer: C

Explanation:
The model card is a governance artifact that communicates intended use, performance characteristics, limitations, fairness considerations, and ethical notes of an ML model.
AAISM governance materials highlight that stakeholder trust comes from transparency and explainability. While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity. Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provide explainability and context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, the model card is the artifact that best enhances trust.


NEW QUESTION # 222
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