Optimised for AI Workloads
Encrypted AI Computing
Mirror Security protects every touchpoint where AI interacts with your data ensuring AI works on encrypted data
Encrypt context windows, conversation history, system prompts, meta-data, vector databases without losing AI capabilities. Offered as SDK for seamless integration with AI Systems
Open-weight models run entirely on encrypted data - inputs, outputs, and model layers remain encrypted end-to-end, enabling secure, private inferences without ever exposing queries or responses in plaintext
Mathematical certainty that your data remains encrypted. Not policy-based security, but cryptographic proof that AI providers cannot access your plaintext data.
Our FHE is specifically optimized for AI workloads, delivering near-native performance while maintaining complete encryption.
Encryption doesn't bloat your storage. Mirror provides noise control knobs via SDK, allowing you to fine-tune the balance between accuracy, latency, and storage space.
Enables the indexing and retrieval of data while it remains fully encrypted. Enable queries on sensitive data without exposing it, maintaining privacy and security without sacrificing the functionality of search systems.
VectaX is offered as an SDK for easy integration, compatible with all major vector databases and open weight LLM models. Drop-in replacement that works with your existing AI stack.
Enterprise-grade security for vector operations with similarity-preserving encryption. Protect your embeddings while maintaining searchability.
Enables the indexing and retrieval of data while it remains fully encrypted. Enable queries on sensitive data without exposing it, maintaining privacy and security without sacrificing the functionality of search systems.
Encrypt sensitive data while maintaining format and searchability. Perfect for securing metadata, PII, and structured data.
Compatible with all leading vector databases, ensuring consistent security enhancements.
Incorporate role-based access controls directly into your vector embeddings multi-dimensional policies.
Control access at role, group, and department levels with comprehensive audit trails.
Protect every phase of your machine learning and RAG workflows, ensuring data integrity and confidentiality from data ingestion through processing.
Compute on encrypted data without ever decrypting it
Code encrypted with your key Never leaves encrypted state
Al processes encrypted data
Mathematical operations preserved
Response remain encrypted
Only you can decrypt
Strengthen defenses against model theft with robust security protocols, ensuring edge based LLM remains exclusive and secure from unauthorized replication or access.
Encrypted inferencing ensures the integrity and confidentiality of data, from input through to output, maintaining its accuracy and preventing tampering or unauthorized disclosure.
Integrate role-based access control in LLMs to secure and regulate user interactions, ensuring compliance and data protection.
Enhance Large Language Models with finetuning that utilizes encrypted data,
ensuring data privacy and compliance while preserving the integrity of the training process.
