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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Governance and Program Management31%- 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 policies and procedures
  • 1. AI-related strategy development
  • 2. Policy implementation and compliance
- Stakeholder considerations and governance frameworks
  • 1. Industry frameworks and regulatory requirements
  • 2. AI governance models and oversight
Topic 2: 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 3: AI Technologies and Controls38%- Trust, safety and compliance controls
  • 1. Responsible AI practices
  • 2. Compliance and governance validation
- AI security monitoring and operations
  • 1. Operational AI controls
  • 2. Security monitoring and logging
- AI security architecture and design
  • 1. AI infrastructure protection
  • 2. Secure AI system architecture
- 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 (Q256-Q261):

NEW QUESTION # 256
To ensure the ethical and responsible use of AI, which of the following AI usage policy metrics is MOST important for an organization to monitor?

Answer: C

Explanation:
AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice. Consult frequency (A) and review cadence (D) are activity metrics, not outcomes. Reported violations (B) are lagging indicators and can be deceptive (low numbers may indicate under-reporting).
References:* AI Security Management (AAISM) Body of Knowledge: Program KPIs-policy adoption, stage-gate compliance, audit readiness* AAISM Study Guide: Governance metrics for Responsible AI- coverage of reviews, pass/fail rates, exceptions handling


NEW QUESTION # 257
Which of the following attacks has the PRIMARY objective of compromising the confidentiality and privacy of information used in an AI system?

Answer: A

Explanation:
Model inversion aims to reconstruct or infer sensitive information from the model's training data, directly compromising confidentiality and privacy.


NEW QUESTION # 258
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

Answer: C

Explanation:
The most effective strategy tominimize attack surfacesin AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle ofleast privilegeto restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
Pre-trained models and log centralization are useful but do not directly reduce the attack surface.Manual code reviewsare important but insufficient against runtime exploitation.
Thus,compartmentalization with least privilege enforcementis the most effective technical safeguard.


NEW QUESTION # 259
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?

Answer: D

Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes: pre- approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records. While MSAs (A) and shared responsibility models (B) set contractual/role baselines, they do not enforce per-change approvals. Data minimization (C) reduces exposure but does not control model substitutions.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Governance: Change Control & Release Management; Third-Party AI Assurance and Approval Workflows; Accountability and Sign-off for Model Changes.


NEW QUESTION # 260
Which of the following is the MOST effective way to mitigate the risk of deepfake attacks?

Answer: C

Explanation:
AAISM study content identifies validating the provenance of data sources as the most effective way to counter deepfake risks. Provenance validation ensures that content is authentic, verifiable, and traceable, preventing malicious synthetic media from being trusted as legitimate. Human oversight helps but cannot reliably detect sophisticated fakes. Limiting tool access reduces exposure but does not prevent external attacks. General-purpose LLMs are not optimized for fraud detection. The strongest control is verifying the origin and authenticity of data before acceptance.
References:
AAISM Study Guide - AI Risk Management (Deepfake and Content Integrity Risks) ISACA AI Security Management - Provenance Validation as a Defense


NEW QUESTION # 261
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