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ISACA AAISM Exam Syllabus Topics:

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q270-Q275):

NEW QUESTION # 270
An organization is reviewing an AI application to determine whether it is still needed. Engineers have been asked to analyze the number of incorrect predictions against the total number of predictions made. Which of the following is this an example of?

Answer: B

Explanation:
AAISM guidance identifies metrics like error rate versus total predictions as a key performance indicator (KPI) for evaluating AI model effectiveness. KPIs provide measurable values to assess performance against objectives. Model validation is broader and occurs prior to production use, testing the model against predefined standards. Control self-assessment relates to governance processes, not predictive accuracy. Explainable decision-making refers to interpretability, not error-rate evaluation. Thus, analyzing incorrect predictions against total predictions is a performance measure, making it a KPI.


NEW QUESTION # 271
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?

Answer: A

Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.


NEW QUESTION # 272
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?

Answer: D

Explanation:
Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.
Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.
References: AI Security Management™ (AAISM) Body of Knowledge: "Operational Resilience-BC/DR for AI Systems," "Validation and Evidence of Continuity"; AAISM Study Guide: "AI DR Test Planning- Metrics, Model Performance Validation, and Recovery Readiness."


NEW QUESTION # 273
The PRIMARY AI governance risk of inconsistently applying data augmentation and cleansing across datasets is that the model may:

Answer: B

Explanation:
Inconsistent data augmentation and cleansing can introduce or amplify bias within datasets, resulting in unreliable or unfair model behavior. This undermines both data integrity and model integrity, creating significant governance, ethical, and operational risks.


NEW QUESTION # 274
Which approach should an organization prioritize to effectively verify the security of its AI models?

Answer: B

Explanation:
The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems. Effective AI security testing requires:
- AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)
- Adversarial attack simulations (white-box, black-box, gradient-based attacks)
- Evaluation of robustness and manipulation resistance


NEW QUESTION # 275
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