Information classification framework according to SOC 2 Type II

Deineka, O. and Harasymchuk, O. and Partyka, A. and Kozachok, Valerii (2024) Information classification framework according to SOC 2 Type II Cybersecurity Providing in Information and Telecommunication Systems II 2024, 3826. pp. 182-189. ISSN 1613-0073

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Abstract

Large Language Models (LLMs) like GPT-3 and BERT, trained on extensive text data, are transforming data management and governance, areas crucial for SOC 2 Type II compliance. LLMs respond to prompts, guiding their output generation, and can automate tasks like data cataloging, enhancing data quality, ensuring data privacy, and assisting in data integration. These capabilities can support a robust data classification policy, a key requirement for SOC 2 Type II. Vector search, another important method in data management, finds similar items to a given item by representing them as vectors in a high-dimensional space. It offers high accuracy, scalability, and flexibility, supporting efficient data classification. Embeddings, which convert categorical data into a form that can be input into a model, play a key role in vector search and LLMs. Prompt engineering, the crafting of effective prompts, is crucial for guiding LLMs’ output, and further enhancing data management and governance practices.

Item Type: Article
Uncontrolled Keywords: SOC 2 Type II; information classification; data security; LLM; vector search; prompt
Subjects: Статті у базах даних > Scopus
Divisions: Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки ім. професора Володимира Бурячка
Depositing User: Павло Миколайович Складанний
Date Deposited: 06 Dec 2024 08:16
Last Modified: 06 Dec 2024 08:16
URI: https://elibrary.kubg.edu.ua/id/eprint/50155

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