The AAISM framework stresses that the most effective way to maintain oversight of organizational AI usage is by maintaining a comprehensive inventory of all AI systems and the business units using them. Such an inventory provides a centralized, transparent record of where AI is deployed, ensuring accountability, monitoring, and compliance. While board approval, dashboards, and KPIs are important governance tools, they do not provide holistic visibility across the enterprise. The inventory ensures traceability and governance alignment, making it the best method to maintain visibility of AI usage.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories) ISACA AI Security Management - Centralized Oversight of AI Assets

The AAISM framework identifies intellectual property (IP) violations as the most significant inherent risk in deploying generative AI. These systems often rely on large-scale internet data for training, which may inadvertently contain copyrighted or proprietary material. This creates legal and reputational exposure when outputs reproduce or reference protected content. While employee training gaps, asset vulnerabilities, and ROI concerns are relevant risks, they are not inherent to generative models themselves. The greatest inherent risk tied directly to generative AI adoption is the possibility of violating intellectual property rights.
References:
AAISM Study Guide - AI Risk Management (Generative AI Risks and Legal Exposure) ISACA AI Security Management - Copyright and IP Concerns in Generative AI

The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor's compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards.
Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.
References:
AAISM Study Guide - AI Risk Management (Third-Party Risk Oversight)
ISACA AI Security Management - Vendor Compliance Monitoring

Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.
References:
AAISM Exam Content Outline - AI Risk Management (Backdoors and Hidden Triggers) AI Security Management Study Guide - Adversarial Training as a Mitigation Control

AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users. This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards.
Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.
References:
AAISM Study Guide - AI Technologies and Controls (Moderation and Output Controls) ISACA AI Security Management - Harmful Content Mitigation in Generative AI