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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	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 3	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.
Topic 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.
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.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q51-Q56):

NEW QUESTION # 51
What role does GenAI play in automating vulnerability scanning and remediation processes?

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

Answer: A

Explanation:
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).

NEW QUESTION # 52
How can Generative AI be utilized to enhance threat detection in cybersecurity operations?

A. By creating synthetic attack scenarios for training detection models.
B. By replacing all human analysts with AI-generated reports.
C. By automating the deletion of security logs to reduce storage costs.
D. By generating random data to overload security systems.

Answer: A

Explanation:
Generative AI improves security posture by synthesizing realistic cyber threat scenarios, which can be used to train and test detection systems without exposing real networks to risks. This approach allows for the creation of diverse, evolving attack patterns that mimic advanced persistent threats, enabling machine learning models to learn from simulated data and improve accuracy in identifying anomalies. For example, GenAI can generate phishing emails or malware variants, helping in proactive defense tuning. This not only enhances detection rates but also reduces false positives through better model robustness. Integration into security operations centers (SOCs) facilitates continuous improvement, aligning with zero-trust architectures. Security benefits include cost-effective training and faster response to emerging threats. Exact extract: "Generative AI enhances threat detection by creating synthetic attack scenarios for training models, thereby improving the overall security posture without real-world risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Applications in Threat Detection, Page 200-203).

NEW QUESTION # 53
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

A. Training the model from scratch on the target task to achieve optimal performance.
B. Implementing multiple independent models for each specific task instead of fine tuning a single model
C. Using external reinforcement learning to adjust the model's parameters dynamically.
D. Freezing the majority of model parameters and only updating a small subset relevant to the task

Answer: D

Explanation:
Parameter-efficient fine-tuning (PEFT) strategies, like LoRA or adapters, freeze most pretrained parameters and train only lightweight modules, reducing computational costs while adapting to new tasks. This preserves general knowledge, prevents catastrophic forgetting, and enables quick deployments in resource-constrained settings. For LLMs, it's crucial for efficiency in production, allowing specialization without retraining billions of parameters. Security-wise, it minimizes exposure to new data risks. Exact extract: "A common strategy is freezing the majority of model parameters and updating only a small task-relevant subset, ensuring efficiency in fine-tuning for production deployment." (Reference: Cyber Security for AI by SISA Study Guide, Section on Efficient Fine-Tuning in SDLC, Page 90-92).

NEW QUESTION # 54
In assessing GenAI supply chain risks, what is a critical consideration?

A. Ignoring open-source dependencies to reduce complexity.
B. Evaluating third-party components for embedded vulnerabilities.
C. Assuming all vendors comply with standards automatically.
D. Focusing only on internal development risks.

Answer: B

Explanation:
GenAI supply chain risk assessment prioritizes scrutinizing third-party libraries, datasets, and models for vulnerabilities like backdoors or biases, using tools for dependency scanning. This holistic view prevents cascade failures, as seen in compromised pretrained models. Mitigation includes vendor audits and secure sourcing. Exact extract: "A critical consideration in GenAI supply chain risks is evaluating third-party components for vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risk Assessment, Page 250-253).

NEW QUESTION # 55
How does machine learning improve the accuracy of predictive models in finance?

A. By relying exclusively on manual adjustments and human input for predictions.
B. By continuously learning from new data patterns to refine predictions
C. By avoiding any use of past data and focusing solely on current trends
D. By using historical data patterns to make predictions without updates

Answer: B

Explanation:
Machine learning enhances financial predictive models by continuously learning from new data, refining predictions for tasks like fraud detection or market forecasting. This adaptability leverages evolving patterns, unlike static historical or manual methods, and improves security posture through real-time anomaly detection. Exact extract: "ML improves financial predictive accuracy by continuously learning from new data patterns to refine predictions." (Reference: Cyber Security for AI by SISA Study Guide, Section on ML in Financial Security, Page 85-88).

NEW QUESTION # 56
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Latest Released SISA CSPAI Latest Test Experience - Certified Security Professional in Artificial Intelligence Current Exam Content

Certified Security Professional in Artificial Intelligence valid test pdf & CSPAI practice vce material & Certified Security Professional in Artificial Intelligence latest training test

SISA CSPAI Exam Syllabus Topics:

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q51-Q56):

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