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

Policy Enforcement

Policy Enforcement

Policy Enforcement

The automated or manual mechanisms that ensure AI operations adhere to organizational policies, regulatory rules, and ethical guidelines.

The automated or manual mechanisms that ensure AI operations adhere to organizational policies, regulatory rules, and ethical guidelines.

Updated

The set of technical controls (e.g., policy-as-code gates, admission controllers, automated audits) and human-review processes that validate compliance at runtime and deployment. Policy enforcement tools intercept model builds, deployments, and API calls, checking against policy definitions (data usage rules, model-risk thresholds) and blocking or flagging any deviations for review.

Example

A financial-services firm codifies its data-retention policy in a policy engine: any storage operation older than the TTL defined for PII fields is automatically purged. If a model-training job attempts to load wiped data, the engine rejects the job and notifies the data-governance team.

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.