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ISACA AAISM Exam Syllabus Topics:

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q248-Q253):

NEW QUESTION # 248
An organization uses a vendor's AI service to screen job applicants. The master service agreement includes a clause related to accountability models. Which of the following is the PRIMARY purpose of this clause in managing AI-specific risk?

Answer: C

Explanation:
Accountability clauses in AI service agreements are primarily intended to clearly define the responsibilities, obligations, and liabilities of each party involved in the use, operation, governance, and oversight of the AI system. This helps manage AI-specific risks related to compliance, security, ethics, and operational failures.


NEW QUESTION # 249
An organization is adopting multiple AI solutions for customer support and management is concerned about potential privacy and confidentiality violations due to the implementation of the new solution. Which of the following is the BEST risk mitigation strategy in this situation?

Answer: D

Explanation:
Establishing responsible AI use policies defines clear expectations for handling data, ethical use, and transparency, directly addressing privacy and confidentiality risks across multiple AI solutions and guiding consistent behavior organization-wide.


NEW QUESTION # 250
Before using data from public domain sources to train generative AI models, it is MOST important to verify that the data source site:

Answer: A

Explanation:
Verifying that the site demonstrates ownership ensures the data is legitimately controlled and legally usable, reducing risks of copyright infringement and unauthorized use when training generative AI models.


NEW QUESTION # 251
When an attacker uses synthetic data to reverse engineer an organization's AI model, it is an example of which of the following types of attack?

Answer: A

Explanation:
Explanation:
Model distillation attacks involve using synthetic or carefully crafted data to query an AI model repeatedly and reconstruct its underlying parameters or behavior. This allows attackers to approximate the original model without direct access, effectively reverse engineering it.


NEW QUESTION # 252
Which of the following patterns MOST likely indicates a potential memory poisoning attack in an agentic AI system?

Answer: B

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 # 253
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