Inside Locktera Confidential RAG

Executive Summary:

Locktera Confidential RAG introduces a new approach to enterprise AI security by enabling high-performance vector similarity search on encrypted data using Fully Homomorphic Encryption (FHE). Built on Locktera’s proprietary computational architecture and working implementation, the technology allows organizations to search sensitive information without exposing searchable vectors during computation.

Designed for deployment on customer-provided GPU infrastructure, Locktera Confidential RAG enables enterprises to maintain control over their computing environments, data governance, and infrastructure costs. Internal benchmarks have demonstrated encrypted search across one million 1,536-dimensional vectors in approximately two seconds under tested configurations.

By combining confidential search with persistent file-level encryption, independent authorization, and auditable access controls, Locktera provides an additional layer of protection for sensitive information throughout supported AI retrieval workflows. Organizations can integrate these capabilities into existing AI applications without replacing their models or surrendering control of their infrastructure.

Locktera Confidential RAG makes high-performance encrypted search practical for enterprise AI while keeping sensitive data protected and infrastructure under customer control.

Dallas, Texas — October 18, 2026 

Locktera Confidential RAG is powered by a proprietary computational architecture and working implementation developed by Locktera specifically for high-performance encrypted vector search.

Using Fully Homomorphic Encryption (FHE), Locktera Confidential RAG enables vector similarity computations to be performed while searchable data remains encrypted. This allows organizations to search sensitive information without exposing the underlying vector representations to the search infrastructure.

Unlike conventional Retrieval-Augmented Generation (RAG) systems that process searchable embeddings in plaintext, Locktera Confidential RAG introduces an additional layer of confidentiality directly into the retrieval process.

Designed for deployment on customer-provided GPU infrastructure, the technology enables enterprises to strengthen AI data security while maintaining control over their computing environments.

Purpose-Built for Confidential Search

Traditional RAG architectures typically rely on vector databases that perform similarity searches on plaintext embeddings. Although these systems may operate within secured cloud environments, sensitive vector representations can still be exposed during computation or through compromised infrastructure.

Locktera takes a different approach.

Our proprietary architecture is designed specifically for the computational demands of FHE, enabling encrypted vector similarity operations without requiring searchable vectors to be decrypted on the search server.

This provides an additional security boundary between sensitive enterprise information and the infrastructure used to search it.

Proprietary Architecture. Working Implementation.

Locktera’s confidential search technology extends beyond a theoretical design.

Our engineering team has developed both the underlying computational architecture and a working implementation that demonstrates encrypted vector search.

The architecture is designed around the computational requirements of encrypted vector operations, with an emphasis on performance, parallel processing, and scalability.

This technology forms the computational foundation of Locktera Confidential RAG and supports continued optimization for enterprise AI workloads.

Bring Your Own GPU Infrastructure

Locktera Confidential RAG is designed for deployment using customer-provided GPU infrastructure.

Rather than requiring organizations to consume a vendor-managed GPU service, Locktera enables customers to deploy confidential search technology within supported computing environments they control.

This deployment model offers several advantages:

• Infrastructure Control: Customers select and operate their own supported GPU infrastructure.
• Deployment Flexibility: Confidential search can be integrated into existing enterprise AI environments.
• Predictable Infrastructure Management: Organizations manage GPU capacity, utilization, and operating costs through their own infrastructure arrangements.
• Data Governance: Customer-controlled deployment can support organizational requirements for data residency, security oversight, and operational control.
• Scalability: Customers can expand supported GPU resources as confidential search workloads increase.

Locktera provides the confidential search technology. Customers provide the GPU infrastructure.

Your GPUs. Your Infrastructure. Locktera Confidential RAG.

Demonstrated Performance for Encrypted Search

Fully Homomorphic Encryption has historically presented computational challenges that can limit its practical adoption in high-performance search applications.

Locktera’s proprietary architecture is designed to address these challenges by optimizing encrypted vector computations.

Internal benchmarks have demonstrated:

• Encrypted search across datasets containing up to 1 million vectors.
• Vector dimensionality of 1,536 dimensions.
• Search execution times of approximately 2 seconds under tested configurations.
• GPU-accelerated computation designed for demanding encrypted-search workloads.

These results demonstrate the potential for confidential vector search to support practical enterprise retrieval applications.

Actual performance depends on GPU configuration, dataset characteristics, concurrency, and deployment architecture. Benchmark results should be evaluated against the specific requirements of each enterprise environment.

More Than Encrypted Search: Persistent Data Protection

Locktera Confidential RAG combines encrypted vector search with Locktera’s persistent file-level security capabilities.

While FHE protects searchable vector representations during similarity computation, Locktera’s data-centric security technology provides additional protection for the underlying source documents.

Together, these capabilities support a layered approach to confidential enterprise AI.

Confidential Vector Search

Perform vector similarity computations on encrypted data without decrypting searchable vectors on the search server.

Protected Source Documents

Maintain sensitive files within Locktera’s encrypted containers, with security policies that remain associated with the protected information.

Independent Authorization

Evaluate applicable access policies before permitting authorized decryption of protected source documents.

Enterprise AI Integration

Incorporate confidential retrieval into supported AI applications, RAG pipelines, and enterprise data workflows without requiring organizations to replace their existing AI models.

Auditability and Accountability

Record supported access requests, authorization outcomes, and policy enforcement events to improve visibility into protected information.

Confidential search protects sensitive representations during the search process. Once retrieved information is legitimately decrypted and released to an AI application, additional safeguards are necessary to govern its subsequent processing and use.

Designed for Enterprise AI Environments

Locktera Confidential RAG is designed to support organizations building AI applications that require access to sensitive or regulated information.

Potential applications include:

Healthcare and Life Sciences

Confidential search across sensitive medical research, clinical documentation, and proprietary scientific information, subject to applicable access and regulatory controls.

Financial Services

Protected retrieval of confidential financial documents, investment research, risk information, and internal business records.

Legal and Intellectual Property

Confidential search across legal documents, patent materials, trade secrets, and privileged business information.

Government and Defense

Encrypted retrieval workflows for sensitive operational information and protected documents within supported, appropriately authorized environments.

Enterprise Knowledge Management

Confidential retrieval across proprietary corporate documents, internal research, technical records, and sensitive business information.

Confidential Search Without Surrendering Infrastructure Control

As enterprises integrate AI into increasingly sensitive workflows, protecting the retrieval process becomes an essential part of the security architecture.

Locktera Confidential RAG addresses this challenge through proprietary encrypted-search technology, customer-controlled GPU infrastructure, and persistent protection for underlying source documents.

Organizations can strengthen their AI retrieval security while retaining control over their infrastructure and integrating with existing applications.

Protect the search. Control access to the data. Keep your infrastructure.

Explore Locktera Confidential RAG

Discover how Locktera’s proprietary FHE technology can help your organization introduce confidential search into enterprise AI workflows.

Explore Locktera Confidential RAG

Watch the Locktera Technical Overview