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SISA CSPAI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Models for Assessing Gen AI Risk: This section of the exam measures skills of the Cybersecurity Risk Manager and deals with frameworks and models used to evaluate risks associated with deploying generative AI. It includes methods for identifying, quantifying, and mitigating risks from both technical and governance perspectives.
Topic 2
  • AIMS and Privacy Standards: ISO 42001 and ISO 27563: This section of the exam measures skills of the AI Security Analyst and addresses international standards related to AI management systems and privacy. It reviews compliance expectations, data governance frameworks, and how these standards help align AI implementation with global privacy and security regulations.
Topic 3
  • Securing AI Models and Data: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on the protection of AI models and the data they consume or generate. Topics include adversarial attacks, data poisoning, model theft, and encryption techniques that help secure the AI lifecycle.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q35-Q40):

NEW QUESTION # 35
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?

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 # 36
What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?

Answer: B

Explanation:
GenAI LLMs rely on data-driven learning, leveraging vast datasets to model language patterns, semantics, and contexts through unsupervised or semi-supervised methods. This enables scalability and adaptability, unlike rule-based systems or human-dependent approaches. Large datasets drive generalization, though they introduce security challenges like data quality control. Exact extract: "A key concept of GenAI LLMs is data- driven learning with large-scale datasets, enabling robust language modeling." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Development Principles, Page 60-63).


NEW QUESTION # 37
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?

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 # 38
What does the OCTAVE model emphasize in GenAI risk assessment?

Answer: A

Explanation:
OCTAVE adapts to GenAI by emphasizing organizational risk perspectives, identifying critical assets like models and data, evaluating threats, and prioritizing mitigations through stakeholder collaboration. It fosters a strategic, enterprise-wide approach to AI risks, integrating business impacts. Exact extract: "OCTAVE emphasizes operational critical threat, asset, and vulnerability evaluation in GenAI risk assessment." (Reference: Cyber Security for AI by SISA Study Guide, Section on OCTAVE for AI, Page 255-258).


NEW QUESTION # 39
In a time-series prediction task, how does an RNN effectively model sequential data?

Answer: D

Explanation:
RNNs model sequential data in time-series tasks by maintaining hidden states that propagate information across time steps, capturing temporal dependencies like trends or seasonality. This memory mechanism allows RNNs to learn from past data, unlike independent processing or holistic approaches, though they face gradient issues for long sequences. Exact extract: "RNNs use hidden states to retain context from prior time steps, effectively capturing dependencies in sequential data for time-series tasks." (Reference: Cyber Security for AI by SISA Study Guide, Section on RNN Architectures, Page 40-43).


NEW QUESTION # 40
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