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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q33-Q38):

NEW QUESTION # 33
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?

A. By replacing each other in different organizational contexts.
B. By focusing ISO 42001 on privacy and ISO 27563 on management.
C. By applying only to public sector AI systems.
D. By combining AI management with privacy standards to address both operational and data protection needs.

Answer: D

Explanation:
The integration of ISO 42001 and ISO 27563 provides a holistic framework: 42001 for overall AI governance and risk management, complemented by 27563's privacy-specific tools, ensuring balanced, compliant AI deployments that protect data while optimizing operations. Exact extract: "ISO 42001 and ISO 27563 integrate to combine AI management with privacy standards for comprehensive governance." (Reference:
Cyber Security for AI by SISA Study Guide, Section on Integrating ISO Standards, Page 280-283).

NEW QUESTION # 34
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?

A. Automatically patching vulnerabilities without additional configuration
B. Reducing the need for manual vulnerability assessment entirely
C. Enabling real-time detection of vulnerabilities with actionable insights.
D. Limiting its use to only high-priority vulnerabilities.

Answer: C

Explanation:
Giskard, an open-source tool, enhances AI security by enabling real-time vulnerability detection, scanning models for issues like bias or adversarial weaknesses, and providing actionable insights for remediation. This proactive approach supports continuous monitoring, unlike automated patching or limited scopes, and integrates into SDLC for robust security. Exact extract: "Giskard enables real-time detection of vulnerabilities with actionable insights, strengthening AI security functions." (Reference: Cyber Security for AI by SISA Study Guide, Section on Vulnerability Detection Tools, Page 190-193).

NEW QUESTION # 35
An AI system is generating confident but incorrect outputs, commonly known as hallucinations. Which strategy would most likely reduce the occurrence of such hallucinations and improve the trustworthiness of the system?

A. Reducing the number of attention layers to speed up generation
B. Retraining the model with more comprehensive and accurate datasets.
C. Encouraging randomness in responses to explore more diverse outputs.
D. Increasing the model's output length to enhance response complexity.

Answer: B

Explanation:
Hallucinations in AI, particularly LLMs, arise from gaps in training data, overfitting, or inadequate generalization, leading to plausible but false outputs. The most effective mitigation is retraining with expansive, high-quality datasets that cover diverse scenarios, ensuring factual grounding and reducing fabrication risks. This involves curating verified sources, incorporating fact-checking mechanisms, and using techniques like data augmentation to fill knowledge voids. Complementary strategies include prompt engineering and external verification, but foundational retraining addresses root causes, enhancing overall trustworthiness. In security contexts, this prevents misinformation propagation, critical for applications in decision-making or content generation. Exact extract: "To reduce hallucinations and improve trustworthiness, retrain the model with more comprehensive and accurate datasets, ensuring better factual alignment and reduced erroneous confidence in outputs." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Risks and Mitigations, Page 120-123).

NEW QUESTION # 36
In the context of LLM plugin compromise, as demonstrated by the ChatGPT Plugin Privacy Leak case study, what is a key practice to secure API access and prevent unauthorized information leaks?

A. Implementing stringent authentication and authorization mechanisms, along with regular security audits
B. Restricting API access to a predefined list of IP addresses
C. Increasing the frequency of API endpoint updates.
D. Allowing open API access to facilitate ease of integration

Answer: A

Explanation:
The ChatGPT Plugin Privacy Leak highlighted vulnerabilities in plugin ecosystems, where weak API security led to data exposure. Implementing robust authentication (e.g., OAuth) and authorization (e.g., RBAC), coupled with regular audits, ensures only verified entities access APIs, preventing leaks. IP whitelisting is less comprehensive, and open access heightens risks. Audits detect misconfigurations, aligning with secure AI practices. Exact extract: "Stringent authentication, authorization, and regular audits are key to securing API access and preventing leaks in LLM plugins." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security Case Studies, Page 170-173).

NEW QUESTION # 37
What is a primary step in the risk assessment model for GenAI data privacy?

A. Limiting assessment to model outputs only.
B. Conducting data flow mapping to identify privacy risks.
C. Ignoring data sources to speed up assessment.
D. Relying on vendor assurances without verification.

Answer: B

Explanation:
Risk assessment for GenAI begins with comprehensive data flow mapping, tracing inputs, processing, and outputs to pinpoint privacy vulnerabilities like unintended data leakage. This step reveals how personal information is handled, enabling classification of risks under frameworks like GDPR or ISO 27701. It facilitates the identification of controls such as anonymization or consent mechanisms. In GenAI, where models infer from vast data, this prevents re-identification attacks. Exact extract: "A primary step in GenAI data privacy risk assessment is conducting data flow mapping to identify and mitigate privacy risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on Privacy Risk Models, Page 235-238).

NEW QUESTION # 38
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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q33-Q38):

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