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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI Threats and Vulnerabilities
|
| AI Governance and Program Management | 31% | - AI Strategy and Policy
|
| AI Technologies and Controls | 38% | - AI Security Architecture
|
Unser ITZert hat langjährige Schulungserfahrungen über IT-Zertifizierungsprüfungen. Die Schulungsunterlagen zur ISACA AAISM Prüfung von ITZert sind zuverlässig. Unser Eliteteam aktualisiert ständig die neuesten Schulungsunterlagen zur ISACA AAISM Prüfung. Unsere Angestelleten haben sich sehr viel Mühe dafür geben, um Ihnen zu helfen, eine gute Note in der Prüfung zu bekommen. Es ist sicher, dass ITZert Ihnen die realen und besten Schulungsunterlagen zur ISACA AAISM Prüfung bietet.
403. Frage
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?
Antwort: B
Begründung:
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.
404. Frage
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which of the following types of attacks is this an example of?
Antwort: A
Begründung:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Prompt Security and Input Manipulation) AI Security Management Study Guide - Threats to Output Integrity
405. Frage
Which phase of the AI data life cycle presents the GREATEST inherent risk?
Antwort: C
Begründung:
AAISM identifies training as the phase with the highest inherent risk because this is where:
* data poisoning can occur
* sensitive data may be exposed
* bias can be introduced
* model inversion risks originate
* security and privacy vulnerabilities are embedded
Preparation (C) carries risk but is less critical. Maintenance (B) and monitoring (A) involve operational safeguards, not foundational risk creation.
References: AAISM Study Guide - AI Data Life Cycle Risks; High-Risk Model Training Phase.
406. Frage
Which of the following MOST effectively secures ongoing stakeholder support for AI initiatives?
Antwort: D
Begründung:
AAISM governance guidance emphasizes that stakeholder buy-in is sustained when the measurable value of AI initiatives is clearly communicated. Value demonstrations include:
* improved efficiency
* reduced cost
* reduced risk
* business growth
Training (B) and risk optimization (C) are important but do not guarantee stakeholder support. A roadmap (D) guides planning but does not secure buy-in.
References: AAISM Study Guide - AI Governance; Stakeholder Engagement & Value Communication.
407. Frage
A financial organization uses AI to detect potential fraudulent activities but is concerned about the impact of potential data poisoning. Which of the following controls would BEST mitigate this risk?
Antwort: D
Begründung:
AAISM identifies training-data diversity and provenance assurance as primary treatments against data poisoning. Sourcing data from multiple, independently governed providers, combined with ingestion validation and anomaly screening, reduces the chance that a single compromised source can skew model behavior and improves cross-source consistency checks. Transparency, break-glass, and awareness are valuable but do not directly reduce poisoning exposure at the training boundary.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Governance & Integrity for AI; Adversarial ML: Poisoning Threats and Mitigations; Supplier and Source Diversification Controls.
408. Frage
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