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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q26-Q31):

NEW QUESTION # 26
How does ISO 27563 support privacy in AI systems?

A. By providing guidelines for privacy-enhancing technologies in AI.
B. By mandating the use of specific encryption algorithms.
C. By limiting AI to non-personal data only.
D. By focusing on performance metrics over privacy.

Answer: A

Explanation:
ISO 27563 offers practical guidance on implementing privacy-enhancing technologies (PETs) in AI, such as differential privacy or federated learning, to protect data while maintaining utility. It addresses risks like inference attacks, ensuring compliance with privacy regulations. Exact extract: "ISO 27563 supports privacy in AI by providing guidelines for privacy-enhancing technologies." (Reference: Cyber Security for AI by SISA Study Guide, Section on ISO 27563 for Privacy, Page 265-268).

NEW QUESTION # 27
What metric is often used in GenAI risk models to evaluate bias?

A. Accuracy rate without considering demographics.
B. Fairness metrics like demographic parity or equalized odds.
C. Computational efficiency during training.
D. Number of parameters in the model.

Answer: B

Explanation:
Bias assessment in GenAI employs fairness metrics such as demographic parity (equal outcomes across groups) or equalized odds (balanced error rates), quantifying disparities in outputs. These metrics guide debiasing techniques, ensuring ethical AI under risk models. In applications like hiring tools, they prevent discriminatory generations, aligning with regulatory requirements. Exact extract: "Fairness metrics like demographic parity are used in GenAI risk models to evaluate and mitigate bias." (Reference: Cyber Security for AI by SISA Study Guide, Section on Bias Assessment Metrics, Page 245-248).

NEW QUESTION # 28
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?

A. Chatbots should have limited conversational abilities to prevent poisoning.
B. Encrypting user data can prevent such attacks
C. Open interaction with users without safeguards can lead to model poisoning and generation of inappropriate content.
D. Continuous live training is essential for enhancing chatbot performance.

Answer: C

Explanation:
The Tay incident, where Microsoft's chatbot was manipulated via toxic inputs to produce offensive content, underscores the dangers of unfiltered live learning, leading to rapid poisoning. Key lesson: Implement safeguards like content filters, rate limits, and moderated feedback loops to prevent adversarial exploitation.
This informs AI security by emphasizing input validation and ethical alignment in interactive systems. Exact extract: "Open interactions without safeguards can lead to model poisoning and inappropriate content, as seen in the Tay case." (Reference: Cyber Security for AI by SISA Study Guide, Section on Case Studies in AI Poisoning, Page 160-163).

NEW QUESTION # 29
In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?

A. The user interface of the AI application
B. The underlying ML model and its training data.
C. The physical hardware running the AI system
D. The marketing materials associated with the AI product

Answer: B

Explanation:
Supply chain attacks in ML exploit vulnerabilities in the ecosystem, with the core ML model and training data being prime targets due to their foundational role in system behavior. Attackers might inject backdoors into pretrained models via compromised libraries (e.g., PyTorch or TensorFlow packages) or poison datasets during sourcing, leading to manipulated outputs or data exfiltration. This is more critical than targeting UI or hardware, as model/data compromises persist across deployments, enabling stealthy, long-term exploits like trojan attacks. Mitigation includes verifying model provenance, using secure repositories, and conducting integrity checks with hashing or digital signatures. In SISA guidelines, emphasis is on end-to-end supply chain auditing to prevent such intrusions, which could result in biased decisions or security breaches in applications like recommendation systems. Protecting these components ensures model reliability and data confidentiality, integral to AI security posture. Exact extract: "In supply chain attacks on machine learning, attackers critically target the underlying ML model and its training data to introduce persistent vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risks in AI, Page 145-148).

NEW QUESTION # 30
How does GenAI contribute to incident response in cybersecurity?

A. By manually reviewing each incident without AI assistance.
B. By focusing only on post-incident reporting.
C. By delaying responses to gather more data for analysis.
D. By automating playbook generation and response orchestration.

Answer: D

Explanation:
GenAI enhances incident response by dynamically generating customized playbooks based on threat intelligence and orchestrating automated actions like isolation or patching. It processes vast logs in real-time, correlating events to prioritize alerts and suggest optimal responses, reducing mean time to respond (MTTR).
For complex incidents, it simulates outcomes of different strategies, aiding decision-making. This automation frees analysts for strategic tasks, improving efficiency and effectiveness in containing breaches. Exact extract:
"GenAI contributes to incident response by automating playbook generation and orchestration, enhancing cybersecurity operations." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Incident Response, Page 215-218).

NEW QUESTION # 31
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Leo King Leo King

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