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You can install and use Exam4Docs SISA exam dumps formats easily and start SISA CSPAI exam preparation right now. The Exam4Docs CSPAI desktop practice test software and web-based practice test software both are the mock Certified Security Professional in Artificial Intelligence (CSPAI) exam that stimulates the actual exam format and content.

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

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 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.

Topic 4

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 5

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.

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Latest CSPAI Test Sample & CSPAI Test Cram Pdf

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

A. Ensuring that AI systems operate safely, ethically, and without causing harm.
B. Developing AI systems with the highest accuracy regardless of data privacy concerns
C. Focusing solely on improving the speed and scalability of AI systems
D. Maximizing model performance while minimizing computational costs.

Answer: A

Explanation:
Responsible AI standards, including ISO 42001 for AI management systems, aim to promote ethical development, ensuring safety, fairness, and harm prevention in LLM deployments. This encompasses bias mitigation, transparency, and accountability, aligning with societal values. Regulations like the EU AI Act reinforce this by categorizing risks and mandating safeguards. The goal transcends performance to foster trust and sustainability, addressing issues like discrimination or misuse. Exact extract: "The primary goal is to ensure AI systems operate safely, ethically, and without causing harm, as outlined in standards like ISO
42001." (Reference: Cyber Security for AI by SISA Study Guide, Section on Responsible AI and ISO Standards, Page 150-153).

NEW QUESTION # 28
What does the OCTAVE model emphasize in GenAI risk assessment?

A. Exclusion of stakeholder input in assessments.
B. Solely technical vulnerabilities in AI models.
C. Short-term tactical responses over strategic planning.
D. Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.

Answer: D

Explanation:
OCTAVE adapts to GenAI by emphasizing organizational risk perspectives, identifying critical assets like models and data, evaluating threats, and prioritizing mitigations through stakeholder collaboration. It fosters a strategic, enterprise-wide approach to AI risks, integrating business impacts. Exact extract: "OCTAVE emphasizes operational critical threat, asset, and vulnerability evaluation in GenAI risk assessment." (Reference: Cyber Security for AI by SISA Study Guide, Section on OCTAVE for AI, Page 255-258).

NEW QUESTION # 29
In what way can GenAI assist in phishing detection and prevention?

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

Answer: D

Explanation:
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).

NEW QUESTION # 30
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

A. Ensuring that AI systems operate safely, ethically, and without causing harm.
B. Developing AI systems with the highest accuracy regardless of data privacy concerns
C. Focusing solely on improving the speed and scalability of AI systems
D. Maximizing model performance while minimizing computational costs.

Answer: A

NEW QUESTION # 31
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

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

Answer: C

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 # 32
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Before starting the SISA CSPAI preparation, plan the amount of time you will allot to each topic, determine the topics that demand more effort and prioritize the components that possess more weightage in the SISA CSPAI Exam. This kind of polished approach is beneficial for a commendable grade in the SISA CSPAI Exam.

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