A vector index is a searchable data structure that lets an AI system find items by similarity instead of exact keyword match. It stores numerical representations, or embeddings, of documents, images, code, or messages, and then uses fast nearest-neighbor search to retrieve content related to a query. This is common in retrieval-augmented generation, semantic search, and chatbot memory systems.
In cyber security, vector indexes matter because they often hold the “knowledge layer” behind AI applications. If an attacker corrupts, deletes, encrypts, or poisons the index, the system may return wrong results, lose recall, or expose sensitive content. A stolen index can also reveal what data the AI has been trained or connected to. Defenses include strict file permissions, network segmentation, backups, integrity checks, and separating AI data stores from exposed application servers.