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Technical SISA CSPAI Training, CSPAI Latest Test Fee

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q29-Q34):

NEW QUESTION # 29
How can Generative AI be utilized to enhance threat detection in cybersecurity operations?

A. By creating synthetic attack scenarios for training detection models.
B. By automating the deletion of security logs to reduce storage costs.
C. By replacing all human analysts with AI-generated reports.
D. By generating random data to overload security systems.

Answer: A

Explanation:
Generative AI improves security posture by synthesizing realistic cyber threat scenarios, which can be used to train and test detection systems without exposing real networks to risks. This approach allows for the creation of diverse, evolving attack patterns that mimic advanced persistent threats, enabling machine learning models to learn from simulated data and improve accuracy in identifying anomalies. For example, GenAI can generate phishing emails or malware variants, helping in proactive defense tuning. This not only enhances detection rates but also reduces false positives through better model robustness. Integration into security operations centers (SOCs) facilitates continuous improvement, aligning with zero-trust architectures. Security benefits include cost-effective training and faster response to emerging threats. Exact extract: "Generative AI enhances threat detection by creating synthetic attack scenarios for training models, thereby improving the overall security posture without real-world risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Applications in Threat Detection, Page 200-203).

NEW QUESTION # 30
What is a primary step in the risk assessment model for GenAI data privacy?

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

Answer: A

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 # 31
During the development of AI technologies, how did the shift from rule-based systems to machine learning models impact the efficiency of automated tasks?

A. Increased system complexity and the requirement for specialized knowledge,
B. Enhanced the precision and relevance of automated outputs with reduced manual tuning.
C. Improved scalability and performance in handling diverse and evolving data.
D. Enabled more dynamic decision-making and adaptability with minimal manual intervention

Answer: D

Explanation:
The transition from rigid rule-based systems, which rely on predefined logic and struggle with variability, to machine learning models introduced data-driven learning, allowing systems to adapt dynamically to new patterns with less human oversight. This shift boosted efficiency in automated tasks by enabling real-time adjustments, such as in spam detection where ML models evolve with threats, unlike static rules. It minimized manual rule updates, fostering scalability and handling complex, unstructured data effectively. However, it introduced challenges like interpretability needs. In GenAI evolution, this paved the way for advanced models like Transformers, impacting sectors by automating nuanced decisions. Exact extract: "The shift enabled more dynamic decision-making and adaptability with minimal manual intervention, significantly improving the efficiency of automated tasks." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Evolution and Impacts, Page 20-23).

NEW QUESTION # 32
How does ISO 27563 support privacy in AI systems?

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

Answer: C

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 # 33
How does machine learning improve the accuracy of predictive models in finance?

A. By relying exclusively on manual adjustments and human input for predictions.
B. By avoiding any use of past data and focusing solely on current trends
C. By using historical data patterns to make predictions without updates
D. By continuously learning from new data patterns to refine predictions

Answer: D

Explanation:
Machine learning enhances financial predictive models by continuously learning from new data, refining predictions for tasks like fraud detection or market forecasting. This adaptability leverages evolving patterns, unlike static historical or manual methods, and improves security posture through real-time anomaly detection. Exact extract: "ML improves financial predictive accuracy by continuously learning from new data patterns to refine predictions." (Reference: Cyber Security for AI by SISA Study Guide, Section on ML in Financial Security, Page 85-88).

NEW QUESTION # 34
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