Yes, vector search can work in air-gapped or on-prem legal environments. The core functionality of vector search—indexing data as numerical vectors and retrieving similar items—does not inherently depend on cloud services or external connectivity. Instead, it relies on algorithms and infrastructure that can be deployed and managed locally. This makes it feasible to implement vector search in environments where data cannot leave a private network or requires strict compliance with legal or security policies.
To set up vector search in air-gapped or on-prem environments, developers can use open-source vector databases like Milvus, FAISS, or Vespa. These tools are designed to run locally, allowing organizations to maintain full control over their data. For example, a legal team could deploy Milvus on an internal Kubernetes cluster, using pre-trained embedding models (like Sentence-BERT or OpenAI’s alternatives, downloaded and stored locally) to convert documents into vectors. The entire pipeline—data ingestion, vectorization, indexing, and querying—would operate within the organization’s private network. Air-gapped setups might require additional steps, such as manually transferring model files or software packages via secure physical media to avoid external network dependencies. This ensures no data is exposed to third-party services or the public internet.
Compliance and security are critical in legal environments. On-prem solutions let organizations enforce access controls, encryption, and audit logging tailored to their policies. For instance, a law firm handling sensitive client documents could use role-based access in Elasticsearch’s vector search plugin to restrict which users can query certain indexed data. Since everything runs locally, there’s no risk of data leakage to external APIs or vendors. Maintenance, such as updating models or scaling infrastructure, would be handled internally, though this requires teams to manage dependencies (like GPU resources for embedding models) and backups. In summary, vector search is viable in restricted environments but demands careful planning around deployment, security, and ongoing management.