AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing. Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
References:
AAISM Exam Content Outline - AI Risk Management (Data Integrity and Protection) AI Security Management Study Guide - Cryptographic Controls for Dataset Integrity

AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Accountability and Liability Management) AI Security Management Study Guide - Responsible Oversight of Agentic AI

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

The AAISM materials classify availability attacks as attempts to disrupt or degrade the functioning of an AI system so that its outputs become unreliable or unusable. In this scenario, the fraudulent social media accounts are deliberately overwhelming the AI tool with misleading negative reviews, undermining its ability to deliver accurate sentiment analysis. This aligns directly with the concept of an availability attack. Model inversion relates to reconstructing training data from outputs, deepfakes involve synthetic content generation, and data poisoning corrupts the training set rather than manipulating inputs at runtime. Therefore, the fraudulent review campaign is most accurately identified as an availability attack.
References:
AAISM Study Guide - AI Risk Management (Adversarial Threats and Availability Risks) ISACA AI Security Management - Attack Classifications

According to the AAISM framework, the first step in drafting an acceptable use policy is defining the scope and intended use of the AI system. This ensures that governance, regulatory considerations, risk assessments, and alignment with organizational policies are all tailored to the specific applications and functions the AI will serve. Once scope and intended use are clearly defined, legal, regulatory, and risk considerations can be systematically applied. Without this step, policies risk being generic and misaligned with business objectives.
References:
AAISM Study Guide - AI Governance and Program Management (Policy Development Lifecycle) ISACA AI Governance Guidance - Defining Scope and Use Priorities