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

Granular Consent

Granular Consent

Granular Consent

A data-privacy approach allowing individuals to grant or deny specific permissions for each type of data use, enhancing transparency and control.

A data-privacy approach allowing individuals to grant or deny specific permissions for each type of data use, enhancing transparency and control.

Updated

Moves beyond blanket “agree/disagree” terms by offering segmented consent options (e.g., location tracking for navigation yes/no; analytics data yes/no). Implemented via consent-management platforms, it requires dynamic consent capture, enforcement in data pipelines, and regular consent audits. Governance must maintain consent records immutable for audits and support easy revocation.

Example

A fitness-tracker app prompts users separately for heart-rate sharing, geolocation, and health analytics. Users can toggle each permission in their settings, and the system enforces those choices at data-ingest points, providing clear audit logs of consent status for each data use.

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.