Enzai MCP server
Connects to your AI.
Any MCP-compatible agent gets the same governed connection, right where your teams already work.
Some of our integrations
Workflow tools
Jira, ServiceNow
Model providers
Anthropic, OpenAI
Agent tools
Copilot Studio, Claude Enterprise
Development environments
Azure AI Foundry, Amazon Bedrock, Databricks

Glossary

Privacy by Design

Privacy by Design

Privacy by Design

An approach that embeds data protection and user privacy considerations into AI system architecture and processes from the outset.

An approach that embeds data protection and user privacy considerations into AI system architecture and processes from the outset.

Updated

A proactive methodology that integrates privacy controls (data minimization, pseudonymization, access controls, encryption) directly into system requirements, design, and deployment. It mandates privacy impact reviews at each development phase, defaulting to the most privacy-protective settings, and ensuring that new features cannot be released without meeting privacy criteria.

Example

A health-tech startup architected its patient-risk prediction tool so that all personal identifiers are tokenized on ingestion, with keys stored separately and access audited. Privacy checks are built into the CI/CD pipeline: any code touching PII automatically fails privacy-gate tests unless explicitly approved by the data-protection officer.

Govern the AI you actually use.

Govern the AI you actually use.

Govern the AI you actually use.

Start with what’s in use.

Start with what’s in use.

Start with what’s in use.

Book a demo and we’ll show you how Enzai finds the AI in use, gives each system an owner and keeps the record.

Book a demo and we’ll show you how Enzai finds the AI in use, gives each system an owner and keeps the record.