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

Model Governance

Model Governance

Model Governance

The policies, roles, and controls that ensure AI models are developed, approved, and used in line with organizational standards and regulatory requirements.

The policies, roles, and controls that ensure AI models are developed, approved, and used in line with organizational standards and regulatory requirements.

Updated

The overarching framework that specifies model-risk policies, defines stakeholder responsibilities (owners, validators, operators), prescribes approval workflows (impact assessments, ethics review), and enforces controls (versioning, access restrictions). Model governance ensures consistency, accountability, and regulatory compliance across all models, from prototype to retirement.

Example

A global insurer enforces model governance by requiring: (1) every new model to complete a Governance Approval Form; (2) quarterly audit of production models for policy adherence; and (3) automatic deprecation of models lacking current approval, ensuring only compliant models operate.

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.