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384. Frage
Which of the following BEST ensures the integrity of data sets used to train AI models?
Antwort: A
Begründung:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing.
Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
385. Frage
Cybersecurity teams should FIRST be embedded in the:
Antwort: C
Begründung:
AAISM stresses that security must be embedded from the earliest phase--design, ensuring:
- threat modeling
- secure architecture
- data protection requirements
- system boundaries
- security-by-design principles
Introducing cybersecurity later increases unmitigated vulnerabilities.
386. 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: D
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.
387. Frage
During red-team testing of an AI system used for lending decisions, which technique BEST simulates a data poisoning attack?
Antwort: C
Begründung:
AAISM defines data poisoning as intentional manipulation of the training data to influence model behavior or outputs. Corrupting training data (D) is the exact definition of this attack type.
388. Frage
Which of the following would BEST help an organization align its AI initiatives with business objectives?
Antwort: B
Begründung:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management™ (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.
389. Frage
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