Protect the complete RAG data path
Treat source documents, chunks, embeddings, metadata, retrieved context, and generated outputs as connected sensitive data layers.
RAG Data Security Without Breaking Vector Search
Protect embeddings, source content, and metadata without breaking vector search, while controlling what sensitive data each human or non-human identity is allowed to receive.
Trusted in production by security & data teams
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What is RAG security?
RAG and vector-search systems increasingly process customer records, financial documents, healthcare data, internal communications, support transcripts, contracts, and other sensitive enterprise content. That data can appear as text chunks, metadata, identifiers, references, numerical embeddings, retrieved context, prompts, logs, and downstream AI outputs.
Where Ubiq fits
RAG security protects the documents, chunks, embeddings, vector stores, retrieval paths, and generated outputs used by retrieval-augmented generation. Ubiq addresses the data-protection layer: it protects sensitive vector records while preserving the geometry similarity search depends on.
Treat embeddings as sensitive derived data
Numerical vectors are not visible plaintext, but they are derived from sensitive content and still require protection and governance.
Pair data protection with broader RAG controls
Use authorization, integrity, ingestion, isolation, deletion, and output-safety controls alongside Ubiq’s data-protection layer.
The vector security tradeoff
Three ways to handle sensitive data in vector search
Two force a choice between security and utility. One does not.
Failed choice 1
Protect semantic content first. Break the search.
1
Sensitive source content
Financial client profile
Maria Chen is a high-net-worth client with annual income of $185,000. She has a conservative risk tolerance and is primarily interested in capital preservation and income-producing investments. Her account ends in 7712.
2
Protect meaningful content before embedding
Financial client profile
8F72-19AC is a 7C2A-9F4B client with annual income of 4E1B-82D9. She has a XK82-VQ4P risk tolerance and is primarily interested in 8F2A-C71B and 4E09-7D22 investments.
3
The embedding model receives meaningless substitutions
The model no longer sees high-net-worth, conservative risk, capital preservation, or income-producing investments. It sees unrelated protected values.
4
Semantic relationships collapse
The vector no longer reliably captures the concepts the search was built to find.
5
Vector search outcome
The data is protected. The search is compromised.
Failed choice 2
Leave it clear. Expose the data.
1
Sensitive source content
Financial client profile
Maria Chen is a high-net-worth client with annual income of $185,000. She has a conservative risk tolerance and is primarily interested in capital preservation and income-producing investments. Her account ends in 7712.
2
Create a normal numerical embedding
Normal embedding
[0.18, -0.42, 0.91, 0.33, -0.67, ...]
The embedding is numerical, not visible plaintext, but it is derived from sensitive content and remains sensitive derived data.
3
Store the normal vector record
Normal embeddingOriginal text chunkMetadataCustomer or document IDSource referenceAccess classification
4
Search works normally
The original vector geometry is intact, so similarity search and downstream computation continue.
5
Vector search outcome
The search works. The sensitive data is exposed.
The Ubiq approach
Protect the data. Preserve the math.
1
Sensitive source content
Financial client profile
Maria Chen is a high-net-worth client with annual income of $185,000. She has a conservative risk tolerance and is primarily interested in capital preservation and income-producing investments. Her account ends in 7712.
2
Protect selected identifiers and create the normal embedding
Selected identifier flow
Account 7712
Ubiq deterministic protection
8F72-19AC
Then embed
Normal stored embedding
[0.18, -0.42, 0.91, 0.33, -0.67, ...]
3
Protect the complete vector record in two parallel lanes
Both lanes work together
Vector lane
Normal stored vector
Ubiq vector transformation
Protected stored vector
Data lane
Source content + metadata
Ubiq data protection
Protected associated data
One protected complete vector record
4
Transform the query vector into the same protected space
Normal query vector
Corresponding Ubiq transformation
Same protected space
Protected similarity search
5
Search the protected vector space
Similarity search continues while the complete vector record stays protected.
How it works
Protect the complete RAG retrieval path
Ubiq transforms stored and query embeddings into the same protected vector space, protects associated source content and metadata, and evaluates identity, context, and dataset policy before returning data to the AI.
Retrieval does not equal authorization.
Finding a relevant record does not automatically make its sensitive data available to the AI. Ubiq evaluates the requesting human or non-human identity and applicable policy before returning the permitted representation of that data.
Where sensitive data meets vector search
Protect complete vector records in the RAG and semantic-search workflows where exposure is unacceptable and retrieval utility is essential.
Enterprise RAG over regulated documents
Protect the text chunks, metadata, references, and embeddings used to retrieve PII, PHI, financial data, contracts, and other regulated enterprise content.
Semantic search across customer records
Search advisor notes, support transcripts, CRM narratives, customer histories, and service summaries without leaving the complete vector record exposed.
Financial-services knowledge retrieval
Protect client profiles, suitability records, account narratives, research, advisor notes, and internal documents used in wealth and financial-services retrieval workflows.
Healthcare and life-sciences retrieval
Protect patient records, clinical notes, research content, trial documents, and sensitive operational material used by RAG and semantic search.
Internal enterprise search
Protect contracts, legal documents, HR records, policies, emails, proprietary research, and internal communications indexed for enterprise retrieval.
Deploy within the existing RAG workflow
Add Ubiq inside the customer environment without replacing the embedding model, application, or vector database.
Integrate at the right layer
Place Ubiq at the library, application, vector, or RAG layer inside the existing architecture.
Keep the existing vector stack
Continue using the current embedding model, query flow, application architecture, and vector database.
Govern protected representations
Protected vectors remain sensitive derived data and should stay governed throughout the workflow.
Where Ubiq fits in the RAG threat model
RAG security spans more than data protection. Ubiq protects sensitive vector records and associated fields. The surrounding RAG system still needs controls for authorization, integrity, ingestion, isolation, deletion, and output safety.
| Threat | Ubiq's role | Complementary control required |
|---|---|---|
| Embedding theft or inversion | Transforms stored and query embeddings and keeps protected representations governed as sensitive derived data. | Access control, network isolation, monitoring, and query-rate limiting. |
| Cleartext chunks and metadata | Encrypts, tokenizes, or masks source chunks, identifiers, metadata, and references separately from the vector transformation. | Data classification, minimization, and retention policy. |
| Unauthorized retrieval | Protects the returned values and can apply identity-driven runtime outcomes through Ubiq data access controls. | Permission-aware pre-filtering, tenant isolation, and retrieval authorization. |
| Cross-tenant leakage | Protects vector records and associated fields under the policies configured for the workload. | Vector-store namespaces, pre-retrieval filters, and isolation testing. |
| Poisoned documents | Not the primary Ubiq control. | Provenance, hashing, scanning, trusted ingestion, and approval workflows. |
| Prompt injection | Not the primary Ubiq control. | Retrieved-content delimiters, validation, policy guardrails, and output checks. |
| Index tampering | Reduces the value of exposed vector data but does not establish index integrity. | Restricted write paths, signed writes, checksums, and integrity monitoring. |
| Deletion and retention | Protects sensitive values while they remain present. | Cascading deletion across source documents, chunks, vectors, logs, and caches. |
Frequently asked questions
Why not encrypt sensitive data before embedding it?
The embedding model needs the original meaning to create useful semantic relationships. Encrypt or tokenize the meaningful content first, and the model sees opaque values instead of the concepts the search depends on. Protecting only a name or account number does not necessarily destroy the embedding. The failure occurs when protection removes the semantically meaningful content required for retrieval.
Why not leave vector embeddings and the connected content unprotected?
The embedding, source chunks, metadata, references, prompts, and retrieval results may all contain or encode sensitive information. The embedding is numerical and does not visibly contain the original text, but it remains sensitive derived data. Search works, but the exposure moves into the vector and AI stack.
Are vector embeddings actually sensitive?
Yes. Embeddings are numerical representations rather than visible cleartext, but they are derived from the original content and can encode sensitive information. They should be treated and governed as sensitive derived data.
What does Ubiq protect in a vector or RAG workflow?
Ubiq protects both parts of the complete vector record: the numerical embedding and the associated source content and metadata. Associated data can include text chunks, identifiers, document or customer IDs, metadata, references, and access classifications.
How does Ubiq protect a vector embedding?
A normal embedding is created first. The Ubiq library or integration transforms it into a computationally protected numerical representation designed to preserve the geometry required for vector operations. The protected representation remains sensitive derived data and should be governed accordingly.
Can the vector database still perform similarity search?
Yes. Stored embeddings and query embeddings receive the corresponding Ubiq transformation, while the existing vector database continues performing its normal mathematical operations on the protected representations.
Does Ubiq replace the embedding model or vector database?
No. Ubiq integrates into the existing library, application, vector, or RAG workflow. Teams keep the embedding model, query flow, and vector database while adding protection around the complete vector record.
Is a protected vector representation no longer sensitive?
No. The representation is computationally difficult to reverse, but it is still derived from sensitive content and should remain governed as sensitive derived data. Protection reduces exposure without making the representation harmless or non-sensitive.
Protect the data connected to every vector
Use the appropriate Ubiq protection method for source chunks, identifiers, metadata, references, and other sensitive fields associated with the embedding.
Field-Level EncryptionEncrypt sensitive source fields and document data connected to the vector record.Vaultless TokenizationProtect identifiers and structured fields without a central token vault or lookup store.Dynamic Data MaskingReduce exposure when protected source content must be displayed or used downstream.