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[Hardware] 2026 Useful Download CSPAI Demo | 100% Free Dump Certified Security Professional

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【Hardware】 2026 Useful Download CSPAI Demo | 100% Free Dump Certified Security Professional

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SISA CSPAI Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q33-Q38):NEW QUESTION # 33
How does GenAI contribute to incident response in cybersecurity?
  • A. By automating playbook generation and response orchestration.
  • B. By focusing only on post-incident reporting.
  • C. By manually reviewing each incident without AI assistance.
  • D. By delaying responses to gather more data for analysis.
Answer: A
Explanation:
GenAI enhances incident response by dynamically generating customized playbooks based on threat intelligence and orchestrating automated actions like isolation or patching. It processes vast logs in real-time, correlating events to prioritize alerts and suggest optimal responses, reducing mean time to respond (MTTR).
For complex incidents, it simulates outcomes of different strategies, aiding decision-making. This automation frees analysts for strategic tasks, improving efficiency and effectiveness in containing breaches. Exact extract:
"GenAI contributes to incident response by automating playbook generation and orchestration, enhancing cybersecurity operations." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Incident Response, Page 215-218).

NEW QUESTION # 34
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. Ignoring the vulnerability if it does not affect core functionalities.
  • B. Immediate public disclosure of the vulnerability.
  • C. Halting all AI projects until a full investigation is complete.
  • D. Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.
Answer: D
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 # 35
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 physical hardware running the AI system
  • B. The marketing materials associated with the AI product
  • C. The underlying ML model and its training data.
  • D. The user interface of the AI application
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 # 36
In a time-series prediction task, how does an RNN effectively model sequential data?
  • A. By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.
  • B. By using hidden states to retain context from prior time steps, allowing it to capture dependencies across the sequence.
  • C. By processing each time step independently, optimizing the model's performance over time.
  • D. By storing only the most recent time step, ensuring efficient memory usage for real-time predictions
Answer: B
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 # 37
What is a key benefit of using GenAI for security analytics?
  • A. Increasing data silos to protect information.
  • B. Limiting analysis to historical data only.
  • C. Predicting future threats through pattern recognition in large datasets.
  • D. Reducing the use of analytics tools to save costs.
Answer: C
Explanation:
GenAI revolutionizes security analytics by mining massive datasets for patterns, predicting emerging threats like zero-day attacks through generative modeling. It synthesizes insights from disparate sources, enabling proactive defenses and anomaly detection with high precision. This foresight allows organizations to allocate resources effectively, preventing breaches before they occur. In practice, it integrates with SIEM systems for enhanced threat hunting. The benefit lies in transforming reactive security into predictive, bolstering posture against sophisticated adversaries. Exact extract: "A key benefit of GenAI in security analytics is predicting future threats via pattern recognition, improving proactive security measures." (Reference: Cyber Security for AI by SISA Study Guide, Section on Predictive Analytics with GenAI, Page 220-223).

NEW QUESTION # 38
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