RAG & Context Pipeline Security refers to solutions designed to secure Retrieval-Augmented Generation (RAG) architectures and the associated data pipelines that provide contextual information to generative AI models. These systems focus on protecting the integrity, confidentiality, and availability of both the retrieval mechanisms and the contextual data sources that inform AI outputs. Core capabilities include monitoring and controlling access to data repositories, validating the provenance and accuracy of retrieved information, and detecting manipulation or leakage within the pipeline. Technical scope often extends to securing API endpoints, enforcing data governance policies, and providing audit trails for data flows between retrieval systems and generative models. Typical users of RAG & Context Pipeline Security solutions are organizations deploying AI-driven applications that rely on external or internal knowledge bases, such as enterprises in regulated industries, research institutions, and technology providers. Their primary goal is to mitigate risks associated with data poisoning, unauthorized access, and the inadvertent exposure of sensitive information during the context retrieval process. This category differs from general AI security by focusing specifically on the unique risks introduced by RAG architectures and their supporting data pipelines, rather than on model robustness or endpoint protection. Cyberin serves as a platform for discovering and comparing RAG & Context Pipeline Security solutions.