AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls.
Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.

Requiring periodic executive review and approval of AI strategy, risk appetite, and major investments ensures continuous alignment between AI initiatives and overall enterprise strategy, enabling governance at the highest level and adapting direction as business priorities evolve.

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

When an AI system experiences an attack after being in production for an extended period, the most effective mitigation strategy is to update the deployed training data with new adversarial data. This process strengthens the model's resilience by retraining it to recognize and resist attack vectors that were previously unknown or unaccounted for. According to the AI Security Management™ (AAISM) framework, risk mitigation for AI systems must address model robustness through adversarial retraining, data quality improvement, and model lifecycle hardening rather than relying solely on reactive measures.
Why Option B is Correct:
* Incorporating adversarial examples into the training set enhances the system's ability to correctly classify and withstand malicious inputs.
* This approach directly mitigates the vulnerability exploited in the attack and supports a proactive, continuous risk management cycle.
Why Other Options Are Incorrect:
* Option A: Monitoring helps detect suspicious activity but does not resolve the underlying vulnerability.
* Option C: Concealing confidence scores may reduce model transparency but does not address the attack mechanism or its root cause.
* Option D: Implementing access controls protects the model's architecture but does not improve model robustness against input manipulation attacks.
Exact Extract from Official AAISM Study Guide:
"AI risk management requires continuous improvement following incidents. After an adversarial or data poisoning event, the preferred risk treatment involves retraining the model using adversarial data and updated datasets to enhance robustness. This ensures the AI model adapts to evolving threat landscapes rather than merely restricting access or obscuring outputs." References:
AI Security Management™ (AAISM) Body of Knowledge: AI Risk Treatment and Mitigation Strategies, Adversarial Robustness and Resilience Engineering.
AI Security Management™ Study Guide: Model Lifecycle Security, Continuous Risk Treatment through Adversarial Retraining.
ISO/IEC 23894:2023, Clause 8.3.2 - Risk treatment through robustness improvement and adversarial data inclusion.

Automated retraining helps maintain AI system robustness by adapting the model to changing data patterns, environmental conditions, and performance drift over time. This enables the system to sustain reliable and accurate behavior in production environments.