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

Data Lifecycle Management

Data Lifecycle Management

Data Lifecycle Management

The policy-based management of data flow throughout its lifecycle: from creation and initial storage to the time it becomes obsolete and is deleted.

The policy-based management of data flow throughout its lifecycle: from creation and initial storage to the time it becomes obsolete and is deleted.

Updated

Defines standardized processes and tools for data ingestion, classification, storage tiering, archival, retention, and secure deletion. Lifecycle governance ensures that data remains accessible and secure when needed, but is purged when obsolete, reducing storage costs, limiting exposure, and supporting compliance with retention regulations.

Example

A retailer’s customer-transaction data is stored in hot databases for six months, then moved to cold storage for two years, and deleted after three years. Automated policies enforce these tiers and generate audit reports confirming deletion, ensuring both business needs and GDPR retention limits are met.

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.