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Quiz 2025 Professional SISA Reliable CSPAI Study Notes

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

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.

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.

Topic 4

Evolution of Gen AI and Its Impact: This section of the exam measures skills of the AI Security Analyst and covers how generative AI has evolved over time and the implications of this evolution for cybersecurity. It focuses on understanding the broader impact of Gen AI technologies on security operations, threat landscapes, and risk management strategies.

Topic 5

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.

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New CSPAI Exam Objectives, CSPAI 100% Accuracy

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

NEW QUESTION # 35
In a financial technology company aiming to implement a specialized AI solution, which approach would most effectively leverage existing AI models to address specific industry needs while maintaining efficiency and accuracy?

A. Adopting a Foundation Model as the base and fine-tuning it with domain-specific financial data to enhance its capabilities for forecasting and risk assessment.
B. Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.
C. Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
D. Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency

Answer: A

Explanation:
Leveraging foundation models like GPT or BERT for fintech involves fine-tuning with sector-specific data, such as transaction logs or market trends, to tailor for tasks like risk prediction, ensuring high accuracy without the overhead of scratch-building. This approach maintains efficiency by reusing pretrained weights, reducing training time and resources in SDLC, while domain adaptation mitigates generalization issues. It outperforms unadapted general models or fragmented specifics by providing cohesive, scalable solutions.
Security is enhanced through controlled fine-tuning datasets. Exact extract: "Adopting a Foundation Model and fine-tuning with domain-specific data is most effective for leveraging existing models in fintech, balancing efficiency and accuracy." (Reference: Cyber Security for AI by SISA Study Guide, Section on Model Adaptation in SDLC, Page 105-108).

NEW QUESTION # 36
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. Retrieving relevant information from the vector database before generating a response
C. Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.
D. Leveraging a diverse set of data sources to enrich the response with varied perspectives

Answer: B

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 # 37
When dealing with the risk of data leakage in LLMs, which of the following actions is most effective in mitigating this issue?

A. Allowing unrestricted access to training data.
B. Using larger datasets to overshadow sensitive information.
C. Applying rigorous access controls and anonymization techniques to training data.
D. Relying solely on model obfuscation techniques

Answer: C

Explanation:
Data leakage in LLMs occurs when sensitive information from training data is inadvertently revealed in outputs, posing privacy risks. Effective mitigation involves strict access controls, such as role-based permissions, and anonymization methods like differential privacy or tokenization to obscure personal data.
These measures prevent extraction attacks while maintaining model utility. Regular audits and data minimization further strengthen defenses. Unlike obfuscation alone, which may not fully protect, combined controls ensure compliance with regulations like GDPR. Exact extract: "Applying rigorous access controls and anonymization techniques to training data is most effective in mitigating data leakage risks in LLMs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Security in AI Models, Page 130-
133).

NEW QUESTION # 38
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 # 39
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

A. Focusing solely on improving the speed and scalability of AI systems
B. Developing AI systems with the highest accuracy regardless of data privacy concerns
C. Ensuring that AI systems operate safely, ethically, and without causing harm.
D. Maximizing model performance while minimizing computational costs.

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
......

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Scott Edwards Scott Edwards

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