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New Reliable CSPAI Test Objectives | Professional CSPAI: Certified Security Professional in Artificial Intelligence 100% Pass

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

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

Improving SDLC Efficiency Using Gen AI: This section of the exam measures skills of the AI Security Analyst and explores how generative AI can be used to streamline the software development life cycle. It emphasizes using AI for code generation, vulnerability identification, and faster remediation, all while ensuring secure development practices.

Topic 3

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 4

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.

Topic 5

Using Gen AI for Improving the Security Posture: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on how Gen AI tools can strengthen an organization’s overall security posture. It includes insights on how automation, predictive analysis, and intelligent threat detection can be used to enhance cyber resilience and operational defense.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q10-Q15):

NEW QUESTION # 10
For effective AI risk management, which measure is crucial when dealing with penetration testing and supply chain security?

A. Prioritize external audits over internal penetration testing to assess supply chain security.
B. Implement penetration testing only for high-risk components and ignore less critical ones
C. Conduct comprehensive penetration testing and continuously evaluate both internal systems and third- party components in the supply chain.
D. Perform occasional penetration testing and only address vulnerabilities in the internal network.

Answer: C

Explanation:
Effective AI risk management requires comprehensive penetration testing and continuous evaluation of both internal and third-party supply chain components to identify vulnerabilities like backdoors or weak APIs. This holistic approach, aligned with SISA risk models, ensures robust security across the AI ecosystem, unlike limited or external-only testing. Exact extract: "Comprehensive penetration testing and continuous evaluation of internal and third-party components are crucial for AI risk management." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Risk Assessment Models, Page 180-183).

NEW QUESTION # 11
In a time-series prediction task, how does an RNN effectively model sequential data?

A. By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.
B. By processing each time step independently, optimizing the model's performance over time.
C. By storing only the most recent time step, ensuring efficient memory usage for real-time predictions
D. By using hidden states to retain context from prior time steps, allowing it to capture dependencies across the sequence.

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 # 12
What does the OCTAVE model emphasize in GenAI risk assessment?

A. Exclusion of stakeholder input in assessments.
B. Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.
C. Solely technical vulnerabilities in AI models.
D. Short-term tactical responses over strategic planning.

Answer: B

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 # 13
In a Retrieval-Augmented Generation (RAG) system, which key step is crucial for ensuring that the generated response is contextually accurate and relevant to the user's question?

A. Integrating advanced search algorithms to ensure the retrieval of highly relevant documents for context.
B. Leveraging a diverse set of data sources to enrich the response with varied perspectives
C. Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.
D. Retrieving relevant information from the vector database before generating a response

Answer: D

Explanation:
In RAG systems, retrieving relevant information from a vector database before generation is pivotal, as it grounds responses in verified, contextually aligned data. Using embeddings and similarity metrics, the system fetches documents matching the query's intent, ensuring accuracy and relevance. While diverse sources or feedback aid long-term improvement, the retrieval step directly drives contextual fidelity, streamlining SDLC by modularizing data access. Exact extract: "Retrieving relevant information from the vector database is crucial for ensuring contextually accurate responses in RAG systems." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Optimization, Page 120-123).

NEW QUESTION # 14
What is a potential risk of LLM plugin compromise?

A. Improved model accuracy
B. Unauthorized access to sensitive information through compromised plugins
C. Reduced model training time
D. Better integration with third-party tools

Answer: B

Explanation:
LLM plugin compromises occur when extensions or integrations, like API-connected tools in systems such as ChatGPT plugins, are exploited, leading to unauthorized data access or injection attacks. Attackers might hijack plugins to leak user queries, training data, or system prompts, breaching privacy and enabling further escalations like lateral movement in networks. This risk is amplified in open ecosystems where plugins handle sensitive operations, necessitating vetting, sandboxing, and encryption. Unlike benefits like accuracy gains, compromises erode trust and invite regulatory penalties. Mitigation strategies include regular vulnerability scans, least-privilege access, and monitoring for anomalous plugin behavior. In AI security, this highlights the need for robust plugin architectures to prevent cascade failures. Exact extract: "A potential risk of LLM plugin compromise is unauthorized access to sensitive information, which can lead to data breaches and privacy violations." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security in LLMs, Page 155-158).

NEW QUESTION # 15
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