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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Governance and Program Management31%- Stakeholder considerations and governance frameworks
  • 1. AI governance models and oversight
  • 2. Industry frameworks and regulatory requirements
- AI asset and data lifecycle management
  • 1. AI asset inventory and management
  • 2. Data governance
- AI policies and procedures
  • 1. AI-related strategy development
  • 2. Policy implementation and compliance
- AI security program management
  • 1. Program development and maintenance
  • 2. Incident response and business continuity
Topic 2: AI Risk Management31%- AI threat and vulnerability management
  • 1. Threat modeling for AI systems
  • 2. AI vulnerability assessment
- AI risk assessment and treatment
  • 1. Risk identification and analysis
  • 2. Risk thresholds and mitigation
- Third-party and supply chain risk management
  • 1. Supply chain security controls
  • 2. Vendor risk evaluation
Topic 3: AI Technologies and Controls38%- AI security monitoring and operations
  • 1. Operational AI controls
  • 2. Security monitoring and logging
- Trust, safety and compliance controls
  • 1. Compliance and governance validation
  • 2. Responsible AI practices
- 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 (Q108-Q113):

NEW QUESTION # 108
Which of the following is the GREATEST risk inherent to implementing generative AI?

Answer: C

Explanation:
The AAISM framework identifies intellectual property (IP) violations as the most significant inherent risk in deploying generative AI. These systems often rely on large-scale internet data for training, which may inadvertently contain copyrighted or proprietary material. This creates legal and reputational exposure when outputs reproduce or reference protected content. While employee training gaps, asset vulnerabilities, and ROI concerns are relevant risks, they are not inherent to generative models themselves. The greatest inherent risk tied directly to generative AI adoption is the possibility of violating intellectual property rights.


NEW QUESTION # 109
An AI research team is developing a natural language processing model that relies on several open-source libraries. Which of the following is the team's BEST course of action to ensure the integrity of the software packages used?

Answer: D

Explanation:
AAISM's technical control guidance emphasizes that when using open-source libraries, the best safeguard for integrity is to scan the packages for malware before installation. This ensures that compromised or malicious code does not enter the AI system environment. Maintaining lists aids consistency but not security. Always using the latest versions may introduce unverified vulnerabilities. Retraining models addresses functionality but not software integrity. Therefore, the strongest protective measure is pre-installation malware scanning of open-source packages.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Software Supply Chain Security) AI Security Management Study Guide - Open-Source Package Risk Mitigation


NEW QUESTION # 110
When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?

Answer: C

Explanation:
The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data. According to the AI Security ManagementTM (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased--reflecting skewed representation, sampling imbalance, or embedded prejudice--the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.


NEW QUESTION # 111
An organization incorporates third-party AI modules into its financial risk evaluation platform.
Which of the following represents the PRIMARY liability concern?

Answer: B

Explanation:
Insufficient explainability is the primary liability concern because financial risk evaluation decisions often require transparency, accountability, and regulatory justification. If third-party AI modules cannot adequately explain how decisions are made, the organization may face legal, compliance, and governance risks.


NEW QUESTION # 112
Secure aggregation enhances federated learning security by:

Answer: D

Explanation:
AAISM explains that secure aggregation ensures the server only sees aggregated model updates--not individual client contributions--so privacy is preserved even if the server is breached.


NEW QUESTION # 113
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