What it means
Embeddings represent meaning as numeric vectors, and vector databases retrieve items based on similarity rather than exact wording.
Why it matters
Similarity can expose sensitive content, return misleading neighbors, or cross tenant and authorization boundaries when indexing is poorly governed.
Practical focus
- Define the approved purpose, accountable owner, users, operating environment, and acceptable risk for embeddings and vector databases before implementation or procurement
- Implement data classification, tenant isolation, authorization-aware retrieval, deletion propagation, index versioning, and relevance testing through documented architecture, policy, configuration, and change control
- Collect index inventories, access tests, deletion verification, retrieval-quality measures, and cross-tenant probes and compare the results with approved requirements, risk thresholds, and expected outcomes
- Exercise this failure scenario as a planned test: a user retrieves semantically similar content that they could not access through the source system
Common mistakes
- Using the label embeddings and vector databases without defining the exact system boundary, decision, data, and people in scope
- Treating a model response or vendor claim as sufficient evidence without testing the surrounding application and operating process
- Failing to preserve index inventories, access tests, deletion verification, retrieval-quality measures, and cross-tenant probes in a form that lets reviewers reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a user retrieves semantically similar content that they could not access through the source system
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: