One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information.

What is patching automation? With increasing numbers of vulnerabilities, there is a growing risk of cyber incidents, attacks and breaches. It’s more important than ever to ensure that your systems are protected 24/7. Patching is a vital step in any vulnerability management process.

In a 2020 paper, Patrick Lewis and his research team introduced the term RAG, or retrieval-augmented generation. This technique enhances generative AI models by utilizing external knowledge sources such as documents and extensive databases. RAG addresses a gap in traditional Large Language Models (LLMs). While traditional models rely on static knowledge already contained within them, RAG incorporates current information that serves as a reliable source of truth for LLMs.

OpenSearch is an open source search and analytics suite that developers use to build solutions for search, data observability, data ingestion, security information and event management (SIEM), vector database, and more. It is designed for scalability, offering powerful full-text search capabilities and supporting various data types, including structured and unstructured data. OpenSearch has rapidly developed into a standalone platform with unique features and capabilities.

Today, computers are more sophisticated, interconnected and complex than ever. This means they’re more capable than ever – but it also comes with a downside: that their attack surface is larger than ever, leading to an elevated risk of cyber incidents. Therefore, your approach to managing vulnerabilities needs to be holistic and prepare for all the ways in which vulnerabilities could cause damage to users, systems or your organization’s operations and reputation.