CSPAI 무료 덤프문제 온라인 액세스

In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?

• A.Enabling real-time detection of vulnerabilities with actionable insights.
• B.Reducing the need for manual vulnerability assessment entirely
• C.Automatically patching vulnerabilities without additional configuration
• D.Limiting its use to only high-priority vulnerabilities.

Which of the following is a method in which simulation of various attack scenarios are applied to analyze the model's behavior under those conditions.

• A.Adversarial testing
• B.Model firewall
• C.Adversarial testing involves systematically simulating attack vectors, such as input perturbations or evasion techniques, to evaluate an AI model's robustness and identify vulnerabilities before deployment. This proactive method replicates real-world threats, like adversarial examples that fool classifiers or prompt manipulations in LLMs, allowing developers to observe behavioral anomalies, measure resilience, and implement defenses like adversarial training or input validation. Unlike passive methods like input sanitation, which cleans data reactively, adversarial testing is dynamic and comprehensive, covering scenarios from data poisoning to model inversion. In practice, tools like CleverHans or ART libraries facilitate these simulations, providing metrics on attack success rates and model degradation. This is crucial for securing AI models, as it uncovers hidden weaknesses that could lead to exploits, ensuring compliance with security standards. By iterating through attack-defense cycles, it enhances overall data and model integrity, reducing risks in high-stakes environments like autonomous systems or financial AI. Exact extract: "Adversarial testing is a method where simulation of various attack scenarios is applied to analyze the model's behavior, helping to fortify AI against potential threats." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Model Security Testing, Page 140-143).
• D.input sanitation
• E.Prompt injections

How does ISO 27563 support privacy in AI systems?

• A.By providing guidelines for privacy-enhancing technologies in AI.
• B.By mandating the use of specific encryption algorithms.
• C.By focusing on performance metrics over privacy.
• D.By limiting AI to non-personal data only.

In assessing GenAI supply chain risks, what is a critical consideration?

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

In a financial technology company aiming to implement a specialized AI solution, which approach would most effectively leverage existing AI models to address specific industry needs while maintaining efficiency and accuracy?

• A.Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
• B.Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
• C.Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.
• D.Adopting a Foundation Model as the base and fine-tuning it with domain-specific financial data to enhance its capabilities for forecasting and risk assessment.