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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies and Controls | 38% | - AI security architecture and design
|
| Topic 2: AI Governance and Program Management | 31% | - Stakeholder considerations and governance frameworks
|
| Topic 3: AI Risk Management | 31% | - AI threat and vulnerability management
|
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NEW QUESTION # 258
A large language model (LLM) has been manipulated to provide advice that serves an attacker's objectives.
Which of the following attack types does this situation represent?
Answer: A
Explanation:
AAISM categorizes the manipulation of an LLM at inference time, where crafted inputs cause outputs to serve attacker objectives, as an evasion attack. Evasion attacks exploit weaknesses in the model's decision- making boundaries by altering queries to produce compromised or misleading outputs. Privilege escalation refers to unauthorized access rights, data poisoning targets the training phase, and model inversion reconstructs training data. In this case, manipulation of outputs to align with an attacker's goals reflects an evasion attack.
References:
AAISM Exam Content Outline - AI Risk Management (Adversarial Attack Types) AI Security Management Study Guide - Evasion and Manipulation Risks
NEW QUESTION # 259
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 # 260
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?
Answer: B
Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance.
Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.
NEW QUESTION # 261
Which of the following BEST ensures AI components are validated during disaster recovery testing?
Answer: A
Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover. The only option that tests actual operational continuity of AI components is:
monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.
NEW QUESTION # 262
A school district contracts a third-party provider for AI-based curriculum recommendations. Which of the following is the BEST way to ensure the vendor uses AI responsibly?
Answer: C
Explanation:
AAISM emphasizes transparency artifacts from vendors to enable due diligence and assurance.
A model card documents intended use, data sources, limitations, performance across subgroups, known risks, and evaluation procedures--information necessary to assess safety, fairness, and compliance for sensitive contexts like education. SSO and support are useful operational features; generic ToS updates are insufficient without model-specific disclosures.
NEW QUESTION # 263
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2026 Latest DumpsActual AAISM PDF Dumps and AAISM Exam Engine Free Share: https://drive.google.com/open?id=1I8el753XBkabektKGarT_NGWivj_uj6h