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

De-identification

De-identification

De-identification

The process of removing or obscuring personal identifiers from data sets, making it difficult to identify individuals, used to protect privacy.

The process of removing or obscuring personal identifiers from data sets, making it difficult to identify individuals, used to protect privacy.

Updated

Techniques include pseudonymization (replacing identifiers with keys), k-anonymity (ensuring records share attribute values), generalization (broadening data granularity), and suppression (omitting sensitive fields). Effective de-identification balances privacy with data utility, and requires re-identification risk assessments under evolving re-identification techniques.

Example

A city publishes anonymized transit logs by replacing user IDs with random tokens and aggregating locations to 1-km grids. Periodic re-identification tests confirm that no individual’s trip can be traced, enabling open data use without compromising rider privacy.

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.