How to secure a deployment for sensitive Cyber Security log analysis?

All answers to this question.

Security is a critical consideration for any deployment involving sensitive data. By default, the standalone version doesn't include complex RBAC, so you must secure the network layer. I recommend deploying it within a private VPC and using a reverse proxy like Nginx for basic authentication. For encryption at rest, you should rely on the underlying storage volume's encryption (like AWS EBS encryption). Always ensure that your Docker environment is hardened and that the API port is not exposed to the public internet to prevent unauthorized access to your vector embeddings.

   Answered 2025-10-12 by Margaret Moore

If we use the Python client locally, does store the embeddings in a way that can be easily intercepted by other local processes?

   Answered 2025-10-14 by Daniel White

• Daniel, when using PersistentClient, the data is stored in a local directory using SQLite. Any user with read access to that directory can see the database files. You should set strict OS-level permissions on the storage folder and consider using full-disk encryption on the host machine to mitigate the risk of local data exfiltration.

     Commented 2025-10-15 by Edward Harris

I found that tokenizing and anonymizing logs before creating embeddings adds an extra layer of privacy for our cybersecurity users.

   Answered 2025-10-17 by Susan Lewis

• Susan is right; PII should never reach the embedding model. This ensures that even if the vector space is compromised, no sensitive personal data can be reconstructed.

     Commented 2025-10-18 by Matthew King