Embedding and Data Exposure

Allowing the storage of vector embeddings without encryption or proper access controls can lead to unauthorized access, data leakage, and potential misuse of sensitive information.

Storing vector embeddings without encryption or proper access controls can lead to unauthorized access and data leakage. Embeddings often represent sensitive information, and their exposure can compromise data confidentiality and integrity.
Example 1: In the following code snippet, a FAISS index containing embeddings is written to a file named `index.faiss`.

import faiss
...

# Generate random embeddings
embeddings = get_llm_embedding()

# Create a FAISS index
index.add(embeddings)

# Write the index to a file
faiss.write_index(index, "index.faiss")
 

If this file is stored without encryption, it becomes susceptible to unauthorized access, potentially exposing sensitive data.