Addressing Security Challenges in AI Systems with Large Language Models

Abstract

Addressing security challenges in AI systems, particularly those involving large language models (LLMs), involves a multi-faceted approach due to the complex nature of these technologies. LLMs, by their design, process vast amounts of data and generate human-like text, which can expose them to various security vulnerabilities. Key challenges include ensuring data privacy, preventing misuse of generated content, and protecting against adversarial attacks. Effective strategies to mitigate these risks include implementing robust access controls, employing advanced encryption methods, and continuously monitoring and updating security protocols. Additionally, incorporating ethical guidelines and fostering transparency in the development and deployment phases are crucial for enhancing the security and trustworthiness of LLM systems. Addressing these issues is essential for maintaining the integrity and reliability of AI applications in diverse domains.