AAISM Valid Test Question & AAISM Real Dump

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ISACA AAISM Exam Syllabus Topics:

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

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q271-Q276):

NEW QUESTION # 271
Which of the following is the MOST critical key risk indicator (KRI) for an AI system?

Answer: C

Explanation:
AAISM highlights that while accuracy and performance metrics are important, the rate of drift is the most critical KRI for AI systems. Model drift occurs when input data or environmental conditions shift, causing the system to degrade and produce unreliable outputs. This risk indicator directly reflects whether the AI continues to function as intended over time. Accuracy rates and response times are performance metrics, not primary risk signals. The amount of data in the model does not reliably indicate exposure to risk. Therefore, the greatest KRI for ongoing assurance and governance is the rate of drift.


NEW QUESTION # 272
Which of the following should be included in an AI acceptable use policy?

Answer: D

Explanation:
An AI acceptable use policy (AUP) sets the organizational expectations and boundaries for how AI systems may be used by employees and third parties. AAISM guidance places emphasis on ethical and legal compliance standards as core elements of an AUP to govern responsible behavior, prevent misuse, and align with regulatory and organizational principles. While data requirements, collection/storage processes, and monitoring may be covered in adjacent standards and procedures (e.g., data management policies, SOPs, and operational runbooks), the AUP's essential function is to codify permissible use anchored to ethics, legality, and organizational values.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Policies and Codes of Conduct; Responsible Use Principles. AAISM Study Guide - Policy Hierarchy and Control Mapping; Acceptable Use and Staff Obligations.


NEW QUESTION # 273
Which of the following is the BEST approach for minimizing risk when integrating acceptable use policies for AI foundation models into business operations?

Answer: D

Explanation:
The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.
References:
AAISM Study Guide - AI Governance and Program Management (Model Lifecycle Governance) ISACA AI Security Guidance - Policies and Lifecycle Management


NEW QUESTION # 274
A bank is training an AI credit scoring model on a vast amount of sensitive customer data. Which of the following is the MOST critical security risk in this training process?

Answer: C


NEW QUESTION # 275
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?

Answer: A

Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance


NEW QUESTION # 276
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