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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	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 3	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 4	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 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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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q44-Q49):

NEW QUESTION # 44
In assessing GenAI supply chain risks, what is a critical consideration?

A. Focusing only on internal development risks.
B. Assuming all vendors comply with standards automatically.
C. Evaluating third-party components for embedded vulnerabilities.
D. Ignoring open-source dependencies to reduce complexity.

Answer: C

Explanation:
GenAI supply chain risk assessment prioritizes scrutinizing third-party libraries, datasets, and models for vulnerabilities like backdoors or biases, using tools for dependency scanning. This holistic view prevents cascade failures, as seen in compromised pretrained models. Mitigation includes vendor audits and secure sourcing. Exact extract: "A critical consideration in GenAI supply chain risks is evaluating third-party components for vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risk Assessment, Page 250-253).

NEW QUESTION # 45
In line with the US Executive Order on AI, a company's AI application has encountered a security vulnerability. What should be prioritized to align with the order's expectations?

A. Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.
B. Ignoring the vulnerability if it does not affect core functionalities.
C. Halting all AI projects until a full investigation is complete.
D. Immediate public disclosure of the vulnerability.

Answer: A

Explanation:
The US Executive Order on AI emphasizes proactive risk management and robust security to ensure safe AI deployment. When a vulnerability is detected, rapid response to remediate it, coupled with a thorough review of security practices, aligns with these mandates by minimizing harm and preventing recurrence. This approach involves patching the issue, assessing root causes, and updating protocols to strengthen defenses, ensuring compliance with standards like ISO 42001, which prioritizes risk mitigation in AI systems. Public disclosure, while important, is secondary to remediation to avoid premature exposure, and halting projects is overly disruptive unless risks are critical. Ignoring vulnerabilities contradicts responsible AI principles, risking regulatory penalties and trust erosion. This strategy fosters accountability and aligns with governance frameworks for secure AI operations. Exact extract: "Addressing vulnerabilities promptly through remediation and reviewing security practices is prioritized to meet the US Executive Order's expectations for safe and secure AI systems." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Governance and US EO Compliance, Page 165-168).

NEW QUESTION # 46
In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?

A. The user interface of the AI application
B. The marketing materials associated with the AI product
C. The underlying ML model and its training data.
D. The physical hardware running the AI system

Answer: C

Explanation:
Supply chain attacks in ML exploit vulnerabilities in the ecosystem, with the core ML model and training data being prime targets due to their foundational role in system behavior. Attackers might inject backdoors into pretrained models via compromised libraries (e.g., PyTorch or TensorFlow packages) or poison datasets during sourcing, leading to manipulated outputs or data exfiltration. This is more critical than targeting UI or hardware, as model/data compromises persist across deployments, enabling stealthy, long-term exploits like trojan attacks. Mitigation includes verifying model provenance, using secure repositories, and conducting integrity checks with hashing or digital signatures. In SISA guidelines, emphasis is on end-to-end supply chain auditing to prevent such intrusions, which could result in biased decisions or security breaches in applications like recommendation systems. Protecting these components ensures model reliability and data confidentiality, integral to AI security posture. Exact extract: "In supply chain attacks on machine learning, attackers critically target the underlying ML model and its training data to introduce persistent vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risks in AI, Page 145-148).

NEW QUESTION # 47
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

A. Tuning the retrieval model to prioritize documents with the highest semantic similarity
B. Utilising an ensemble of multiple LLMs to cross-check the generated outputs.
C. Implementing a redundancy check by comparing the outputs from different retrieval modules.
D. Fine-tuning the generative model with synthetic datasets generated from the retrieved documents

Answer: A

Explanation:
The Retrieval-Augmented Generation (RAG) framework enhances generative models by incorporating external knowledge retrieval to ground outputs in factual data, thereby improving consistency and reducing hallucinations. The critical factor lies in optimizing the retrieval component to select documents with maximal semantic relevance, often using techniques like dense vector embeddings (e.g., via BERT or similar encoders) and similarity metrics such as cosine similarity. This ensures that the generator receives contextually precise information, minimizing irrelevant or misleading inputs that could lead to inconsistent outputs. For instance, in question-answering systems, prioritizing high-similarity documents allows the model to reference verified sources directly, boosting accuracy. Other approaches, like ensembles or redundancy checks, are supplementary but less foundational than effective retrieval tuning, which directly impacts the quality of augmented context. In SDLC, integrating RAG with fine-tuned retrieval accelerates development cycles by enabling modular updates without full model retraining. Security benefits include tracing outputs to sources for auditability, aligning with responsible AI practices. This method scales well for large knowledge bases, making it essential for production-grade applications where factual integrity is paramount. Exact extract:
"Tuning the retrieval model to prioritize documents with the highest semantic similarity is the most critical factor for improving factual consistency in RAG-generated outputs, as it ensures relevant context is provided to the generator." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Frameworks in SDLC Efficiency, Page 95-98).

NEW QUESTION # 48
What is a primary step in the risk assessment model for GenAI data privacy?

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

Answer: B

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 # 49
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CSPAI–100% Free New Practice Materials | Trustable Examcollection Certified Security Professional in Artificial Intelligence Vce

SISA CSPAI Exam Syllabus Topics:

The Best Accurate New CSPAI Practice Materials - Win Your SISA Certificate with Top Score

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q44-Q49):

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