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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.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which of the following describes the scenario where an LLM is embedded 'As-is' into an application frame?

A. Customizing the LLM to fit specific application requirements and workflows before integration.
B. Replacing the LLM with a more specialized model tailored to the application's needs.
C. Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.
D. Using the LLM solely for backend data processing, while the application handles all user interactions.

Answer: C

Explanation:
Embedding an LLM 'as-is' means direct integration of the pretrained model into the app framework without alterations, relying on its inherent capabilities for tasks like text generation, simplifying SDLC by avoiding customization overhead. This is suitable for general-purpose apps but may lack optimization for specifics, contrasting with tailored approaches. It accelerates deployment while posing risks like unmitigated biases, necessitating post-integration safeguards. Exact extract: "It describes integrating the LLM without modifications, using out-of-the-box capabilities directly in the application." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Integration Methods, Page 110-113).

NEW QUESTION # 32
In transformer models, how does the attention mechanism improve model performance compared to RNNs?

A. By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies
B. By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.
C. By enhancing the model's ability to process data in parallel, ensuring faster training without compromising context.
D. By processing each input independently, ensuring the model captures all aspects of the sequence equally.

Answer: A

Explanation:
Transformer models leverage self-attention to process entire sequences concurrently, unlike RNNs, which handle inputs sequentially and struggle with long-range dependencies due to vanishing gradients. By computing attention scores across all words, Transformers capture both local and global contexts, enabling better modeling of relationships in tasks like translation or summarization. For example, in a long sentence, attention links distant pronouns to their subjects, improving coherence. This contrasts with RNNs' sequential limitations, which hinder capturing far-apart dependencies. While parallelism (option C) aids efficiency, the core improvement lies in dependency modeling, not just speed. Exact extract: "The attention mechanism enables Transformers to attend to nearby and distant words simultaneously, significantly improving long-term dependency understanding over RNNs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer vs. RNN Architectures, Page 50-53).

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

A. By avoiding any use of past data and focusing solely on current trends
B. By using historical data patterns to make predictions without updates
C. By relying exclusively on manual adjustments and human input for predictions.
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
In a time-series prediction task, how does an RNN effectively model sequential data?

A. By storing only the most recent time step, ensuring efficient memory usage for real-time predictions
B. By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.
C. By processing each time step independently, optimizing the model's performance over time.
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 # 35
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?

A. Encrypting user data can prevent such attacks
B. Open interaction with users without safeguards can lead to model poisoning and generation of inappropriate content.
C. Chatbots should have limited conversational abilities to prevent poisoning.
D. Continuous live training is essential for enhancing chatbot performance.

Answer: B

Explanation:
The Tay incident, where Microsoft's chatbot was manipulated via toxic inputs to produce offensive content, underscores the dangers of unfiltered live learning, leading to rapid poisoning. Key lesson: Implement safeguards like content filters, rate limits, and moderated feedback loops to prevent adversarial exploitation.
This informs AI security by emphasizing input validation and ethical alignment in interactive systems. Exact extract: "Open interactions without safeguards can lead to model poisoning and inappropriate content, as seen in the Tay case." (Reference: Cyber Security for AI by SISA Study Guide, Section on Case Studies in AI Poisoning, Page 160-163).

NEW QUESTION # 36
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