Yes, you can limit exposure of private product metadata in vector search by combining data isolation, access controls, and selective indexing. The core idea is to ensure sensitive information is either excluded from the vector search index or encrypted and accessible only to authorized users. This requires a combination of technical safeguards and system design choices to maintain search functionality while protecting sensitive data.
First, separate sensitive metadata from non-sensitive data during indexing. For example, if a product database contains public descriptions and private supplier pricing, only the public descriptions should be embedded into vectors for search. The private pricing data can be stored in a separate, secured database linked by a unique identifier. When a vector search returns results, your system can fetch the associated private metadata only after verifying the user’s permissions. This approach ensures the vector index itself contains no sensitive data, reducing exposure. Tools like PostgreSQL with its row-level security or cloud-native solutions like AWS DynamoDB with fine-grained access controls can enforce this separation.
Second, apply role-based access controls (RBAC) or attribute-based encryption (ABE) to limit who can retrieve private metadata. For instance, a customer-facing e-commerce app might use vector search to find products based on public attributes like color or size. When the search results are returned, the backend could check the user’s role (e.g., “customer” vs. “admin”) before attaching private metadata like cost or profit margins to the response. Encryption techniques like field-level encryption (e.g., using AWS KMS or Google Cloud KMS) can also ensure that even if private metadata is stored alongside vectors, it remains unreadable without proper decryption keys.
Finally, use anonymization or pseudonymization for metadata that must remain in the vector index but needs protection. For example, replace raw customer review scores with aggregated sentiment scores (e.g., “positive” or “negative”) to hide exact ratings. Alternatively, tokenize sensitive fields like product SKUs by mapping them to random strings before indexing. Open-source libraries like Presidio or commercial tools like Microsoft’s Azure Cognitive Services can automate this process. By designing the system to handle these tokens or aggregated values during search, you maintain functionality without exposing raw private data. Regularly audit the system to ensure no accidental leakage occurs through indirect patterns, such as vectors inadvertently encoding sensitive metadata.