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SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q16-Q21):

NEW QUESTION # 16
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson learned from this case study?

A. Encrypting user data can prevent such attacks
B. Chatbots should have limited conversational abilities to prevent poisoning.
C. Continuous live training is essential for enhancing chatbot performance.
D. Open interaction with users without safeguards can lead to model poisoning and generation of inappropriate content.

Answer: D

Explanation:
The Tay incident, where Microsoft's chatbot was manipulated via toxic inputs to produce offensive content, underscores the dangers of unfiltered live learning, leading to rapid poisoning. Key lesson: Implement safeguards like content filters, rate limits, and moderated feedback loops to prevent adversarial exploitation.
This informs AI security by emphasizing input validation and ethical alignment in interactive systems. Exact extract: "Open interactions without safeguards can lead to model poisoning and inappropriate content, as seen in the Tay case." (Reference: Cyber Security for AI by SISA Study Guide, Section on Case Studies in AI Poisoning, Page 160-163).

NEW QUESTION # 17
In a time-series prediction task, how does an RNN effectively model sequential data?

A. By using hidden states to retain context from prior time steps, allowing it to capture dependencies across the sequence.
B. By processing each time step independently, optimizing the model's performance over time.
C. By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.
D. By storing only the most recent time step, ensuring efficient memory usage for real-time predictions

Answer: A

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 # 18
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 user interface of the AI application
B. The physical hardware running the AI system
C. The marketing materials associated with the AI product
D. The underlying ML model and its training data.

Answer: D

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 # 19
In what way can GenAI assist in phishing detection and prevention?

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

Answer: C

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 # 20
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

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

Answer: C

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 # 21
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100% Pass Quiz High Hit-Rate SISA - CSPAI - Certified Security Professional in Artificial Intelligence Online Lab Simulation

SISA CSPAI Exam Syllabus Topics:

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