In today’s tech landscape, the buzz around AI is deafening. From GitHub Copilot revolutionizing code creation to internal chatbots streamlining support operations, the power of Large Language Models (LLMs) is propelling us into unparalleled realms of efficiency at breakneck speed. Innovations like retrieval-augmented generation (RAG) have elevated LLMs by integrating them with internal knowledge bases, rendering them contextually aware and significantly more user-friendly.
Despite the allure of these advancements, a critical concern looms large: the management of secrets, especially concerning the burgeoning cohort of non-human identities (NHIs). As highlighted in a compelling piece on understanding NHIs governance, neglecting to rein in your secrets could potentially accelerate your security incident frequency, overshadowing any productivity gains your team might achieve through AI adoption.
Before rushing to deploy that enticing new LLM or linking essential platforms like Jira, Confluence, or internal API documentation to your chat-based assistant, it’s imperative to address the often underestimated threat of secrets sprawl and the uncharted territory of ungoverned NHIs.