Securing the Future of AI with Vector Intelligence

Unlock new horizons by partnering
with MIrror Security

Industry-first encryption technology that protects your code while maintaining full AI productivity

Unlock new horizons by partnering
with Mirror Security

Whether you’re a cybersecurity distributor safeguarding your clients’ AI or seeking a strategic tech partnership to integrate robust AI security, partnering with us will enable you to contribute to a future of safe and trustworthy AI.

Become a Partner

Become a Partner

What is a Vector Database?

What is a Vector Database?

A Vector Database (Vector DB) is purpose-built to store and query high-dimensional vector embeddings—mathematical representations of unstructured data like text, images, audio, and video. These embeddings power the intelligence behind AI systems by enabling fast and accurate semantic search and contextual retrieval, especially for LLMs (Large Language Models) and GenAI applications. 


Unlike traditional databases, vector DBs allow systems to understand meaning, not just keywords—making them essential for modern AI pipelines. 

Usage of Vector DBs

Usage of Vector DBs

As AI shifts from structured to unstructured data, vector databases have become foundational to: 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

The Challenge

The Challenge

Security, Privacy & Compliance Risks

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Data Breaches

Vector databases often store sensitive embeddings (PII, PHI, financial data) without sufficient encryption, making them targets for attackers.

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Data Breaches

Vector databases often store sensitive embeddings (PII, PHI, financial data) without sufficient encryption, making them targets for attackers.

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Inference Attacks

Advanced attackers can reverse-engineer embeddings to reconstruct original data, breaching user privacy and intellectual property.

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Inference Attacks

Advanced attackers can reverse-engineer embeddings to reconstruct original data, breaching user privacy and intellectual property.

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Lack of Data Residency Controls

Many AI pipelines don’t meet jurisdictional data handling laws (GDPR, HIPAA), especially when embeddings cross borders or clouds.

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Lack of Data Residency Controls

Many AI pipelines don’t meet jurisdictional data handling laws (GDPR, HIPAA), especially when embeddings cross borders or clouds.

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Blind Spot in Traditional Security

Existing encryption techniques protect data at rest and in transit, but not during computation—leaving a critical attack surface wide open.

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Blind Spot in Traditional Security

Existing encryption techniques protect data at rest and in transit, but not during computation—leaving a critical attack surface wide open.

Usage of Vector DBs

Usage of Vector DBs

We solve this with FHE (Fully Homomorphic Encryption)—an advanced cryptographic method that allows data to remain encrypted even while being queried or processed. 


Benefits of FHE in AI pipelines: 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

Our Solutions: Secure Vector Intelligence Platform

Our Solutions: Secure Vector Intelligence Platform

At Mirror Security, we offer a Zero Trust, FHE-powered Vector Intelligence Platform designed to secure the AI data layer at every step. 

FHE-Based Secure Vector Search

FHE-Based Secure Vector Search

  • Perform high-speed, encrypted similarity search. 

  • Store and retrieve vector embeddings with full privacy. 

  • Protect LLM inputs and outputs from reverse engineering. 

  • Perform high-speed, encrypted similarity search. 

  • Store and retrieve vector embeddings with full privacy. 

  • Protect LLM inputs and outputs from reverse engineering. 

  • Perform high-speed, encrypted similarity search. 

  • Store and retrieve vector embeddings with full privacy. 

  • Protect LLM inputs and outputs from reverse engineering. 

Agent-Aware Guardrails

Agent-Aware Guardrails

  • Fine-grained security policies for AI agents, prompts, and RAG pipelines. 

  • Define what data agents can see, access, or respond with—ensuring compliance and brand safety. 

Multi-Party and Multi-Cloud Compatibility

Multi-Party and Multi-Cloud Compatibility

  • Plug-and-play with Pinecone, Weaviate, Qdrant, Milvus, and open-source alternatives. 

  • Hybrid support for AWS, Azure, GCP, and on-prem deployments. 

  • Interoperable with OpenAI, Hugging Face, Cohere, and custom embedding models. 

Governance, Auditing, and Policy Engine

Governance, Auditing, and Policy Engine

  • Define role-based access controls, encryption keys, and data lineage tracking. 

  • Full audit logs for every vector query or LLM response—ensuring traceability and compliance. 

Performance-Driven with Proven Benchmarks 

  • <50ms average latency on encrypted vector queries 

  • 99.9% search accuracy preservation 

  • Up to 10x faster than conventional encrypted search methods 

Developer SDKs & Application Integration

Developer SDKs & Application Integration

  • SDKs and APIs for rapid integration into GenAI apps 

  • Seamless integration with LangChain, LlamaIndex, and custom RAG frameworks 

  • Prebuilt wrappers for Python, TypeScript, Go, and Rust 

Platform-Level Data Security (RRR Framework™) 

Our RRR Framework (Recon Remediate Reinforce) offers complete lifecycle protection: 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

Power intelligent search across documents, chats, and multimedia. 

Know more

Know more

Backed by Benchmark & Real-World Validation 

We continuously benchmark our platform across regulated and high-risk environments: 

Metric

Result

Encrypted Query Latency

<50ms

Accuracy Degradation

<0.1%

Supported Vector Size

up to 16,384 dims

Cloud Compatibility

AWS, Azure, GCP, Hybrid

Supported Vector DBs

Pinecone, Milvus, Weaviate, Qdrant, Vespa

Our customers span industries from healthcare to finance, ensuring data remains secure even under strict compliance regimes. 

Mirror Security

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