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CSPAI證照資訊，CSPAI資料

不同的方式是可以達到相同的目的的，就看你選擇什麼樣的方式，走什麼樣的路。很多人都想通過SISA CSPAI 認證考試來使自己的工作和生活有所提升，但是參加過SISA CSPAI 認證考試的人都知道通過SISA CSPAI 認證考試不是很簡單。有的人為了能通過SISA CSPAI 認證考試花費了很多寶貴的時間和精力卻沒有成功。

SISA CSPAI 考試大綱：

主題
簡介

主題 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.

主題 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.

主題 3

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.

主題 4

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.

>> CSPAI證照資訊 <<

CSPAI資料 - CSPAI題庫分享

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最新的 Cyber Security for AI CSPAI 免費考試真題 (Q23-Q28):

問題 #23
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 marketing materials associated with the AI product
B. The physical hardware running the AI system
C. The user interface of the AI application
D. The underlying ML model and its training data.

答案：D

解題說明：
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).

問題 #24
What role does GenAI play in automating vulnerability scanning and remediation processes?

A. By ignoring low-priority vulnerabilities to focus on high-impact ones.
B. By generating code patches and suggesting fixes based on vulnerability descriptions.
C. By increasing the frequency of manual scans to ensure thoroughness.
D. By compiling lists of vulnerabilities without any analysis.

答案：B

解題說明：
GenAI automates vulnerability management by analyzing scan results and generating tailored code patches or remediation strategies, accelerating the fix process and reducing human error. Using natural language processing, it interprets vulnerability reports, cross-references with known exploits, and proposes secure code alternatives, integrating seamlessly into DevSecOps pipelines. This proactive approach minimizes exposure windows and enhances system resilience against exploits. For instance, in cloud environments, GenAI can simulate patch impacts before application. This contributes to a stronger security posture by enabling rapid, accurate responses to threats. Exact extract: "GenAI automates vulnerability scanning and remediation by generating code patches and fixes, improving efficiency and security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on Automation in Vulnerability Management, Page 205-208).

問題 #25
In what way can GenAI assist in phishing detection and prevention?

A. By generating realistic phishing simulations and analyzing user responses.
B. By sending automated phishing emails to test employee awareness.
C. By relying solely on signature-based detection methods.
D. By blocking all incoming emails to prevent any potential threats.

答案：A

解題說明：
GenAI bolsters phishing defenses by creating sophisticated simulation campaigns that mimic real attacks, training employees and refining detection algorithms based on interaction data. It analyzes email content, URLs, and attachments semantically to identify subtle manipulations, going beyond traditional filters. This dynamic method adapts to evolving tactics like AI-generated deepfakes in emails, improving prevention through predictive modeling. Organizations benefit from reduced successful breach rates and enhanced user education. Integration with email gateways provides real-time alerts, strengthening overall security. Exact extract: "GenAI assists in phishing detection by generating simulations and analyzing responses, thereby preventing attacks and improving security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI in Phishing Mitigation, Page 210-213).

問題 #26
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?

A. Prioritizing transparency and accountability in AI systems to avoid high-risk categorization
B. Ensuring the AI system meets stringent privacy standards to protect sensitive data
C. Implementing measures to prevent any harmful outcomes and ensure AI system safety
D. Focusing on integrating ethical guidelines to ensure AI decisions are fair and unbiased.

答案：C

解題說明：
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).

問題 #27
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.

答案：D

解題說明：
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).

問題 #28
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