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NEW QUESTION # 261
An AI fraud detection system frequently flags legitimate transactions as suspicious, leading to customer dissatisfaction and operational obstacles. Which of the following is the BEST way to mitigate this issue without compromising security?
Answer: A
Explanation:
Conducting a model review helps identify why legitimate transactions are being incorrectly flagged and enables improvements to model accuracy. Adding explainability techniques also helps analysts and stakeholders better understand decision outcomes, reducing operational issues while maintaining effective fraud detection capabilities.
NEW QUESTION # 262
An attack has occurred on an AI system that has been in use for two years. Which of the following would BEST mitigate the impact of the attack?
Answer: D
Explanation:
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.
NEW QUESTION # 263
A retail organization implements an AI-driven recommendation system that utilizes customer purchase history. Which of the following is the BEST way for the organization to ensure privacy and comply with regulatory standards?
Answer: A
Explanation:
According to the AI Security ManagementTM (AAISM) study framework, compliance with privacy and regulatory standards must begin with a formalized process of identifying, documenting, and maintaining applicable obligations. The guidance explicitly notes that organizations should maintain a comprehensive register of legal and regulatory requirements to ensure accountability and alignment with privacy laws. This register serves as the foundation for all governance, risk, and control practices surrounding AI systems that handle personal data.
Maintaining such a register ensures that the recommendation system operates under the principles of privacy by design and privacy by default. It allows decision-makers and auditors to trace every AI data processing activity back to relevant compliance obligations, thereby demonstrating adherence to laws such as GDPR, CCPA, or other jurisdictional mandates.
NEW QUESTION # 264
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.
NEW QUESTION # 265
When using data augmentation to balance an underrepresented class in a training dataset, which of the following is the GREATEST long-term risk to the AI model's real-world performance?
Answer: A
Explanation:
Data augmentation can introduce synthetic patterns that the model may learn too specifically, leading to overfitting. This reduces the model's ability to generalize to real-world data, increasing the likelihood of unexpected failures in production.
NEW QUESTION # 266
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