Glean for Secure Enterprise AI

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Purpose-Built for Governance and Control

Governance

Protection

Governance-First Architecture

Powered by Proxima’s TwinGuard™ framework, the platform is architected for policy-enforced access, auditability, and deep data governance, establishing trust before any retrieval or productivity layers were even introduced.

Data Exposure Protection

While other tools rely on inherited app-level permissions, Proxima enforces access rules directly at the data layer to prevent leakage and minimize exposure.

Insider Threat Detection

Proxima continuously monitors user behavior and query patterns to detect and block unauthorized access attempts in real time.

Cost Efficiency

With support for open-source LLMs, modular pipelines, and flexible deployments, Proxima helps reduce operational costs without sacrificing control.

MCP-Native Architecture

Proxima’s unified MCP framework makes it easy to connect internal and external agents, copilots, and automation platforms like LangGraph, allowing real-time, secure, and policy-aware data access.

Built for Regulated Environments

Proxima supports frameworks like FFIEC, GLBA, and HIPAA out of the box, making it ideal for sectors that demand auditability, explainability, and fine-grained access control.

Security-First by Design

Proxima enforces least-privilege access, real-time auditing, and compliance from day one, not as an afterthought.

// FAQ’s

We’re here to answer your questions

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What happens if someone tries to access data they shouldn't?

Daxa enforces permissions and governance policies at the data and retrieval layer, ensuring only authorized, role-appropriate context reaches the AI application or agent. Unauthorized access attempts can also be monitored and audited.

What if our existing applications already have permission issues?

Daxa adds an additional governance layer to your AI data flows. Its Safe Connectors capture existing permissions, while Safe Retriever applies identity, semantic, and policy controls before data reaches the AI model.

Can Daxa work with our existing AI applications and infrastructure?

Yes. Daxa can be integrated into existing RAG applications, AI applications, and agentic workflows through its SDK, APIs, or MCP integrations. It can be deployed within your existing environment so enterprise data remains under your control.

Does Daxa work with our existing LLMs and AI models?

Yes. Daxa is designed to sit across the AI data flow rather than lock you into a specific model. It can govern the data retrieved for your application and route inference through policy-approved models or adapters.

How does Daxa protect against sensitive data leakage?

Daxa classifies and tags enterprise data during ingestion, captures existing permissions, and enforces access controls at retrieval time. This ensures sensitive or restricted data is filtered before it reaches the AI application or model.

Built for Regulated AI, Engineered for Trusted Access

Purpose-Built for Governance and Control

Governance-First Architecture

Data Exposure Protection

Insider Threat Detection

Cost Efficiency

MCP-Native Architecture

Built for Regulated Environments

Security-First by Design

Secure AI Agents.
Real Business Value.

Regulatory Research Agents

KYC/AML Automation

Financial Document Q&A

Clinical Decision Support

Secure AI Starts Here

We’re here to answer your questions

Unlock Rapid ROI with Daxa

Built for Regulated AI, Engineered for Trusted Access

Purpose-Built for Governance and Control

Governance-First Architecture

Data Exposure Protection

Insider Threat Detection

Cost Efficiency

MCP-Native Architecture

Built for Regulated Environments

Security-First by Design

Secure AI Agents. Real Business Value.

Regulatory Research Agents

KYC/AML Automation

Financial Document Q&A

Clinical Decision Support

Secure AI Starts Here

We’re here to answer your questions

Unlock Rapid ROI with Daxa

Secure AI Agents.
Real Business Value.