The rapid rise of large language models (LLMs) and generative AI has presented new challenges for security teams everywhere. In creating new ways for data to be accessed, gen AI doesn’t fit traditional security paradigms focused on preventing data from going to people who aren’t supposed to have it. To enable organizations to move quickly on gen AI without introducing undue risk, security providers need to update their programs, taking into account the new types of risk and how they put pressure on their existing programs. An entire industry is currently being built and expanded on top of LLMs hosted by such services as OpenAI, Hugging Face and Anthropic. In addition, there are a number of open models available such as LLaMA from Meta and GPT-2 from OpenAI. Access to these models could help employees in an organization solve business challenges. But for a variety of reasons, not everybody is in a position to access these models directly. Instead, employees often look for tools — such as browser extensions, SaaS productivity applications, Slack apps and paid APIs — that promise easy use of the models.
Full story : Generative AI: A pragmatic blueprint for data security.
