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Title: Latest CSPAI Learning Material - Test CSPAI Vce Free [Print This Page]
Author: jonreed449    Time: yesterday 04:29
Title: Latest CSPAI Learning Material - Test CSPAI Vce Free
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SISA CSPAI Exam Syllabus Topics:
TopicDetails
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
  • 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 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.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q28-Q33):NEW QUESTION # 28
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.
Answer: D

NEW QUESTION # 29
A company developing AI-driven medical diagnostic tools is expanding into the European market. To ensure compliance with local regulations, what should be the company's primary focus in adhering to the EU AI Act?
Answer: B
Explanation:
The EU AI Act classifies AI systems by risk, with medical diagnostics as high-risk, requiring stringent safety measures to prevent harm, such as misdiagnoses. Compliance prioritizes robust testing, validation, and monitoring to ensure safe outcomes, aligning with ISO 42001's risk management framework. While ethics and privacy are critical, safety is the primary focus to meet regulatory thresholds and protect users. Exact extract: "The EU AI Act emphasizes implementing measures to prevent harmful outcomes and ensure AI system safety, particularly for high-risk applications like medical diagnostics." (Reference: Cyber Security for AI by SISA Study Guide, Section on EU AI Act Compliance, Page 175-178).

NEW QUESTION # 30
An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?
Answer: B
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 # 31
In a time-series prediction task, how does an RNN effectively model sequential data?
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 # 32
What is a potential risk of LLM plugin compromise?
Answer: D
Explanation:
LLM plugin compromises occur when extensions or integrations, like API-connected tools in systems such as ChatGPT plugins, are exploited, leading to unauthorized data access or injection attacks. Attackers might hijack plugins to leak user queries, training data, or system prompts, breaching privacy and enabling further escalations like lateral movement in networks. This risk is amplified in open ecosystems where plugins handle sensitive operations, necessitating vetting, sandboxing, and encryption. Unlike benefits like accuracy gains, compromises erode trust and invite regulatory penalties. Mitigation strategies include regular vulnerability scans, least-privilege access, and monitoring for anomalous plugin behavior. In AI security, this highlights the need for robust plugin architectures to prevent cascade failures. Exact extract: "A potential risk of LLM plugin compromise is unauthorized access to sensitive information, which can lead to data breaches and privacy violations." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security in LLMs, Page 155-158).

NEW QUESTION # 33
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