An embedding model converts text into a numeric vector, or embedding, that preserves semantic meaning. Similar phrases end up close together in vector space, which lets systems compare prompts by intent instead of exact wording. This is the core primitive behind semantic search, clustering, deduplication, and semantic caching.
In cyber security, embeddings help defenders group repeated or paraphrased requests, detect abusive prompt patterns, and reuse prior safe responses without paying for duplicate inference. They also support incident triage by clustering logs, tickets, or chat messages with similar intent. The same capability can be abused: an attacker can paraphrase prompts to evade exact-match filters or probe whether a cache reuses answers across similar requests. Because embeddings are approximate, security teams must tune similarity thresholds, version models carefully, and avoid reusing stale or unsafe results.