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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q39-Q44):
NEW QUESTION # 39
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?
- A. Developing AI systems with the highest accuracy regardless of data privacy concerns
- B. Maximizing model performance while minimizing computational costs.
- C. Ensuring that AI systems operate safely, ethically, and without causing harm.
- D. Focusing solely on improving the speed and scalability of AI systems
Answer: C
Explanation:
Responsible AI standards, including ISO 42001 for AI management systems, aim to promote ethical development, ensuring safety, fairness, and harm prevention in LLM deployments. This encompasses bias mitigation, transparency, and accountability, aligning with societal values. Regulations like the EU AI Act reinforce this by categorizing risks and mandating safeguards. The goal transcends performance to foster trust and sustainability, addressing issues like discrimination or misuse. Exact extract: "The primary goal is to ensure AI systems operate safely, ethically, and without causing harm, as outlined in standards like ISO
42001." (Reference: Cyber Security for AI by SISA Study Guide, Section on Responsible AI and ISO Standards, Page 150-153).
NEW QUESTION # 40
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. Limiting its use to only high-priority vulnerabilities.
- C. Reducing the need for manual vulnerability assessment entirely
- D. Enabling real-time detection of vulnerabilities with actionable insights.
Answer: D
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 # 41
What is a primary step in the risk assessment model for GenAI data privacy?
- A. Relying on vendor assurances without verification.
- B. Ignoring data sources to speed up assessment.
- C. Conducting data flow mapping to identify privacy risks.
- D. Limiting assessment to model outputs only.
Answer: C
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 # 42
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 # 43
A company developing AI-driven medical diagnostic tools is expanding into the European market. To ensure compliance with local regulations, what should be the company's primary focus in adhering to the EU AI Act?
- A. Focusing on integrating ethical guidelines to ensure AI decisions are fair and unbiased.
- B. Implementing measures to prevent any harmful outcomes and ensure AI system safety
- C. Prioritizing transparency and accountability in AI systems to avoid high-risk categorization
- D. Ensuring the AI system meets stringent privacy standards to protect sensitive data
Answer: B
Explanation:
The EU AI Act classifies AI systems by risk, with medical diagnostics as high-risk, requiring stringent safety measures to prevent harm, such as misdiagnoses. Compliance prioritizes robust testing, validation, and monitoring to ensure safe outcomes, aligning with ISO 42001's risk management framework. While ethics and privacy are critical, safety is the primary focus to meet regulatory thresholds and protect users. Exact extract: "The EU AI Act emphasizes implementing measures to prevent harmful outcomes and ensure AI system safety, particularly for high-risk applications like medical diagnostics." (Reference: Cyber Security for AI by SISA Study Guide, Section on EU AI Act Compliance, Page 175-178).
NEW QUESTION # 44
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