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SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 (Q14-Q19):

NEW QUESTION # 14
An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?

A. Enhanced model adaptability to diverse data types.
B. Increased model efficiency in processing and generation tasks.
C. Compromised model integrity and reliability leading to inaccurate or biased outputs
D. Improved model performance due to higher data volume.

Answer: C

Explanation:
Poisoned datasets introduce adversarial perturbations or malicious samples that, when used in training, can subtly alter a model's decision boundaries, leading to degraded integrity and unreliable outputs. This risk manifests as backdoors or biases, where the model performs well on clean data but fails or behaves maliciously on triggered inputs, compromising security in applications like classification or generation. For instance, in a facial recognition system, poisoned data might cause misidentification of certain groups, resulting in biased or inaccurate results. Mitigation involves rigorous data validation, anomaly detection, and diverse sourcing to ensure dataset purity. The consequence extends to ethical concerns, potential legal liabilities, and loss of trust in AI systems. Addressing this requires ongoing monitoring and adversarial training to bolster resilience. Exact extract: "Using poisoned datasets can compromise model integrity, leading to inaccurate, biased, or manipulated outputs, which undermines the reliability of AI systems and poses significant security risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Poisoning Risks, Page 112-115).

NEW QUESTION # 15
Which framework is commonly used to assess risks in Generative AI systems according to NIST?

A. A general IT risk assessment without AI-specific considerations.
B. The AI Risk Management Framework (AI RMF) for evaluating trustworthiness.
C. Focusing solely on financial risks associated with AI deployment.
D. Using outdated models from traditional software risk assessment.

Answer: B

Explanation:
The NIST AI Risk Management Framework (AI RMF) provides a structured approach to identify, assess, and mitigate risks in GenAI, emphasizing trustworthiness attributes like safety, fairness, and explainability. It categorizes risks into governance, mapping, measurement, and management phases, tailored for AI lifecycles.
For GenAI, it addresses unique risks such as hallucinations or bias amplification. Organizations apply it to conduct impact assessments and implement controls, ensuring compliance and ethical deployment. Exact extract: "NIST's AI RMF is commonly used to assess risks in Generative AI, focusing on trustworthiness and lifecycle management." (Reference: Cyber Security for AI by SISA Study Guide, Section on NIST Frameworks for AI Risk, Page 230-233).

NEW QUESTION # 16
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?

A. Increasing the frequency of API endpoint updates.
B. Implementing stringent authentication and authorization mechanisms, along with regular security audits
C. Restricting API access to a predefined list of IP addresses
D. Allowing open API access to facilitate ease of integration

Answer: B

Explanation:
The ChatGPT Plugin Privacy Leak highlighted vulnerabilities in plugin ecosystems, where weak API security led to data exposure. Implementing robust authentication (e.g., OAuth) and authorization (e.g., RBAC), coupled with regular audits, ensures only verified entities access APIs, preventing leaks. IP whitelisting is less comprehensive, and open access heightens risks. Audits detect misconfigurations, aligning with secure AI practices. Exact extract: "Stringent authentication, authorization, and regular audits are key to securing API access and preventing leaks in LLM plugins." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security Case Studies, Page 170-173).

NEW QUESTION # 17
How does the multi-head self-attention mechanism improve the model's ability to learn complex relationships in data?

A. By forcing the model to focus on a single aspect of the input at a time.
B. By ensuring that the attention mechanism looks only at local context within the input
C. By simplifying the network by removing redundancy in attention layers.
D. By allowing the model to focus on different parts of the input through multiple attention heads

Answer: D

Explanation:
Multi-head self-attention enhances a model's capacity to capture intricate patterns by dividing the attention process into multiple parallel 'heads,' each learning distinct aspects of the relationships within the data. This diversification enables the model to attend to various subspaces of the input simultaneously-such as syntactic, semantic, or positional features-leading to richer representations. For example, one head might focus on nearby words for local context, while another captures global dependencies, aggregating these insights through concatenation and linear transformation. This approach mitigates the limitations of single- head attention, which might overlook nuanced interactions, and promotes better generalization in complex datasets. In practice, it results in improved performance on tasks like NLP and vision, where multifaceted relationships are key. The mechanism's parallelism also aids in scalability, allowing deeper insights without proportional computational increases. Exact extract: "Multi-head attention improves learning by permitting the model to jointly attend to information from different representation subspaces at different positions, thus capturing complex relationships more effectively than a single attention head." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer Mechanisms, Page 48-50).

NEW QUESTION # 18
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?

A. Encouraging randomness in responses to explore more diverse outputs.
B. Increasing the model's output length to enhance response complexity.
C. Retraining the model with more comprehensive and accurate datasets.
D. Reducing the number of attention layers to speed up generation

Answer: C

Explanation:
Hallucinations in AI, particularly LLMs, arise from gaps in training data, overfitting, or inadequate generalization, leading to plausible but false outputs. The most effective mitigation is retraining with expansive, high-quality datasets that cover diverse scenarios, ensuring factual grounding and reducing fabrication risks. This involves curating verified sources, incorporating fact-checking mechanisms, and using techniques like data augmentation to fill knowledge voids. Complementary strategies include prompt engineering and external verification, but foundational retraining addresses root causes, enhancing overall trustworthiness. In security contexts, this prevents misinformation propagation, critical for applications in decision-making or content generation. Exact extract: "To reduce hallucinations and improve trustworthiness, retrain the model with more comprehensive and accurate datasets, ensuring better factual alignment and reduced erroneous confidence in outputs." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Risks and Mitigations, Page 120-123).

NEW QUESTION # 19
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[New Launch] SISA CSPAI Dumps (Practice Test) with Newly CSPAI Exam

SISA CSPAI Exam Syllabus Topics:

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q14-Q19):

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