SISA Cyber Security for AI CSPAI Question # 19 Topic 2 Discussion
CSPAI Exam Topic 2 Question 19 Discussion:
Question #: 19
Topic #: 2
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.
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.
B.
Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency
C.
Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.
D.
Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.
Leveraging foundation models like GPT or BERT for fintech involves fine-tuning with sector-specific data, such as transaction logs or market trends, to tailor for tasks like risk prediction, ensuring high accuracy without the overhead of scratch-building. This approach maintains efficiency by reusing pretrained weights, reducing training time and resources in SDLC, while domain adaptation mitigates generalization issues. It outperforms unadapted general models or fragmented specifics by providing cohesive, scalable solutions. Security is enhanced through controlled fine-tuning datasets. Exact extract: "Adopting a Foundation Model and fine-tuning with domain-specific data is most effective for leveraging existing models in fintech, balancing efficiency and accuracy." (Reference: Cyber Security for AI by SISA Study Guide, Section on Model Adaptation in SDLC, Page 105-108).
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