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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which of the following patterns MOST likely indicates a potential memory poisoning attack in an agentic AI system?

Answer: A

Explanation:
Memory poisoning attacks involve malicious manipulation or corruption of an AI agent's memory or stored context. Anomalous high-frequency writes are a strong indicator because they may reflect repeated unauthorized attempts to alter memory contents, inject false context, or corrupt the agent's retained information.


NEW QUESTION # 84
The PRIMARY goal of data poisoning attacks is to:

Answer: D

Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine- tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production.


NEW QUESTION # 85
Which of the following BEST addresses hallucination risk when adopting generative AI solutions?

Answer: D

Explanation:
Mandatory human review ensures that AI-generated outputs are checked for accuracy and reliability before use, directly mitigating hallucination risk by preventing incorrect or fabricated information from being accepted or acted upon.


NEW QUESTION # 86
Which of the following BEST describes the role of model cards in AI solutions?

Answer: B

Explanation:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of-scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.


NEW QUESTION # 87
After an organization deploys an AI-based system, a regulator warns of increased risk related to AI re-identification attacks on anonymized datasets. Which of the following should the information security manager do FIRST?

Answer: D

Explanation:
Establishing continuous monitoring with privacy audits and adversarial testing directly addresses the evolving risk of re-identification by detecting vulnerabilities and measuring the effectiveness of anonymization over time. This proactive approach enables the organization to assess and mitigate the risk without unnecessarily disrupting operations.


NEW QUESTION # 88
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