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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Risk Management31%- Third-party and supply chain risk management
  • 1. Vendor risk evaluation
  • 2. Supply chain security controls
- AI risk assessment and treatment
  • 1. Risk thresholds and mitigation
  • 2. Risk identification and analysis
- AI threat and vulnerability management
  • 1. Threat modeling for AI systems
  • 2. AI vulnerability assessment
Topic 2: AI Governance and Program Management31%- AI asset and data lifecycle management
  • 1. AI asset inventory and management
  • 2. Data governance
- AI policies and procedures
  • 1. Policy implementation and compliance
  • 2. AI-related strategy development
- AI security program management
  • 1. Incident response and business continuity
  • 2. Program development and maintenance
- 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 architecture and design
  • 1. AI infrastructure protection
  • 2. Secure AI system architecture
- Trust, safety and compliance controls
  • 1. Compliance and governance validation
  • 2. Responsible AI practices
- AI security monitoring and operations
  • 1. Operational AI controls
  • 2. Security monitoring and logging
- 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 (Q162-Q167):

NEW QUESTION # 162
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:

Answer: A

Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.


NEW QUESTION # 163
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls.
Which of the following types of attacks is this an example of?

Answer: A

Explanation:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.


NEW QUESTION # 164
An organization is leveraging a third-party AI model to bring to market a new AI-enabled software solution. Which of the following represents the MOST significant risk in this scenario?

Answer: C

Explanation:
Ambiguous intellectual property rights and ineffective AI governance create significant legal, operational, and compliance risks when using third-party AI models. Unclear ownership, usage rights, accountability, and governance responsibilities can directly impact the organization's ability to securely and lawfully commercialize the AI-enabled solution.


NEW QUESTION # 165
An aerospace manufacturer prioritizing accuracy and security wants to use generative AI. Which LLM adoption plan BEST aligns with its risk appetite?

Answer: C

Explanation:
AAISM notes that high-security industries (e.g., aerospace) should prefer private, controlled environments with restricted data exposure. Developing a private LLM for non-critical workloads minimizes operational and security risk while enabling innovation.
Public LLMs for critical functions (C) violate safety expectations. Purchased datasets (D) introduce unknown provenance. Outsourcing (B) increases third-party risk.
References: AAISM Study Guide - AI Governance; Safe LLM Adoption Strategies.


NEW QUESTION # 166
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

Answer: B

Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections


NEW QUESTION # 167
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