Data Security Risk Assessment and Response Strategy for Large Language Models

Abstract

In the era of artificial intelligence, large language models (LLMs) feature both positives and negatives when it comes to data security. The purpose of this paper is to present detailed recommendations for evaluating and managing the risks of data security connected with LLMs, using contemporary artificial intelligence algorithms and cloud-based information technologies. These are data asset categorization and rating, risk assessment models, and the observation of legal requirements and best practices regarding data safety. Key findings’ main message is the criticality of the systematic approach to establish and assess controls against data leakage and compliance risks. Besides, the paper also stresses the importance of effective comprehensiveness of report analysis as well as the integration of security capabilities that will help strengthen the overall security of an enterprise. Lastly, based on the highlights of the paper, the best practices for enterprises to address the risks associated with data security are outlined to provide practical and effective measures of protection as well as compliance, particularly in today’s fast-evolving technological environment of AI technologies.