Updated SISA CSPAI Dumps – Check Free CSPAI Exam Dumps (2026) [Q22-Q37]

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Updated SISA CSPAI Dumps – Check Free CSPAI Exam Dumps (2026)

Updated CSPAI exam with SISA Real Exam Questions

Q22. In transformer models, how does the attention mechanism improve model performance compared to RNNs?

By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies

By processing each input independently, ensuring the model captures all aspects of the sequence equally.

By enhancing the model’s ability to process data in parallel, ensuring faster training without compromising context.

By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.

Q23. When integrating LLMs using a Prompting Technique, what is a significant challenge in achieving consistent performance across diverse applications?

Handling the security concerns that arise from dynamically generated prompts

Overcoming the lack of transparency in understanding how the LLM interprets varying prompt structures.

The need for optimizing prompt templates to ensure generalization across different contexts.

Reducing latency in generating responses to meet real-time application requirements.

Q24. What is a potential risk of LLM plugin compromise?

Better integration with third-party tools

Improved model accuracy

Unauthorized access to sensitive information through compromised plugins

Reduced model training time

Q25. How does GenAI contribute to incident response in cybersecurity?

By delaying responses to gather more data for analysis.

By automating playbook generation and response orchestration.

By manually reviewing each incident without AI assistance.

By focusing only on post-incident reporting.

Q26. How does the STRIDE model adapt to assessing threats in GenAI?

By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to AI components.

By focusing only on hardware threats in AI systems.

By excluding AI-specific threats like model inversion.

By using it unchanged from traditional software.

Q27. When dealing with the risk of data leakage in LLMs, which of the following actions is most effective in mitigating this issue?

Applying rigorous access controls and anonymization techniques to training data.

Using larger datasets to overshadow sensitive information.

Allowing unrestricted access to training data.

Relying solely on model obfuscation techniques

Q28. An AI system is generating confident but incorrect outputs, commonly known as hallucinations. Which strategy would most likely reduce the occurrence of such hallucinations and improve the trustworthiness of the system?

Retraining the model with more comprehensive and accurate datasets.

Reducing the number of attention layers to speed up generation

Increasing the model’s output length to enhance response complexity.

Encouraging randomness in responses to explore more diverse outputs.

Q29. Which of the following describes the scenario where an LLM is embedded ‘As-is’ into an application frame?

Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.

Replacing the LLM with a more specialized model tailored to the application’s needs.

Customizing the LLM to fit specific application requirements and workflows before integration.

Using the LLM solely for backend data processing, while the application handles all user interactions.

Q30. In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

Fine-tuning the generative model with synthetic datasets generated from the retrieved documents

Utilising an ensemble of multiple LLMs to cross-check the generated outputs.

Implementing a redundancy check by comparing the outputs from different retrieval modules.

Tuning the retrieval model to prioritize documents with the highest semantic similarity

Q31. What is a common use of an LLM as a Secondary Chatbot?

To serve as a fallback or supplementary AI assistant for more complex queries

To replace the primary AI system

To handle tasks unrelated to the main application

To only manage user credentials

Q32. In what way can GenAI assist in phishing detection and prevention?

By sending automated phishing emails to test employee awareness.

By generating realistic phishing simulations and analyzing user responses.

By blocking all incoming emails to prevent any potential threats.

By relying solely on signature-based detection methods.

Q33. What is a key benefit of using GenAI for security analytics?

Increasing data silos to protect information.

Predicting future threats through pattern recognition in large datasets.

Limiting analysis to historical data only.

Reducing the use of analytics tools to save costs.

Q34. How does the multi-head self-attention mechanism improve the model’s ability to learn complex relationships in data?

By forcing the model to focus on a single aspect of the input at a time.

By ensuring that the attention mechanism looks only at local context within the input

By simplifying the network by removing redundancy in attention layers.

By allowing the model to focus on different parts of the input through multiple attention heads

Q35. 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?

Restricting API access to a predefined list of IP addresses

Increasing the frequency of API endpoint updates.

Implementing stringent authentication and authorization mechanisms, along with regular security audits

Allowing open API access to facilitate ease of integration

Q36. How can Generative AI be utilized to enhance threat detection in cybersecurity operations?

By generating random data to overload security systems.

By creating synthetic attack scenarios for training detection models.

By automating the deletion of security logs to reduce storage costs.

By replacing all human analysts with AI-generated reports.

Q37. Which of the following is a method in which simulation of various attack scenarios are applied to analyze the model’s behavior under those conditions.

input sanitation

Model firewall

Prompt injections

Adversarial testing

Adversarial testing involves systematically simulating attack vectors, such as input perturbations or evasion techniques, to evaluate an AI model’s robustness and identify vulnerabilities before deployment. This proactive method replicates real-world threats, like adversarial examples that fool classifiers or prompt manipulations in LLMs, allowing developers to observe behavioral anomalies, measure resilience, and implement defenses like adversarial training or input validation. Unlike passive methods like input sanitation, which cleans data reactively, adversarial testing is dynamic and comprehensive, covering scenarios from data poisoning to model inversion. In practice, tools like CleverHans or ART libraries facilitate these simulations, providing metrics on attack success rates and model degradation. This is crucial for securing AI models, as it uncovers hidden weaknesses that could lead to exploits, ensuring compliance with security standards. By iterating through attack-defense cycles, it enhances overall data and model integrity, reducing risks in high-stakes environments like autonomous systems or financial AI. Exact extract: “Adversarial testing is a method where simulation of various attack scenarios is applied to analyze the model’s behavior, helping to fortify AI against potential threats.” (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Model Security Testing, Page 140-143).

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	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 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	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	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.