Jerry Petersen
Protecting sensitive private data in large language models, or LLMs, is increasingly important amid the growing adoption of artificial intelligence and machine learning, according to Gina Scinta, deputy chief technology officer at Thales Trusted Cyber Technologies.
Securing Data in LLMs
Scinta said in a column published Monday on Intelligence Community News that securing data in LLMs has two use cases. The first involves protecting data at rest and in transit, while the second involves protecting data at rest, in transit and in execution.
Concerning the first case, Scinta favors using a data-centric security platform. Such a platform works to centralize and simplify data security. It reduces the amount of resources needed to ensure security, accelerates compliance and even ensures the safe migration of sensitive information to the cloud.
Concerning the second case, which is a cloud infrastructure issue, the deputy CTO favors using end-to-end data protection and recommends that it be centrally managed across cloud service providers.
Demand for Enhanced Protection
“The threat posed to data privacy and security in LLMs demands enhanced protection for query understanding, information retrieval, and contextual responses. By implementing stringent data protection strategies, such as robust access controls and transparent encryption, organizations can mitigate the risks associated with LLM use cases,” Scinta said.
