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

Multi-Stakeholder Engagement

Multi-Stakeholder Engagement

Multi-Stakeholder Engagement

Involving diverse groups (e.g., legal, ethics, operations, end users) in AI governance processes to ensure balanced risk oversight and alignment with business goals.

Involving diverse groups (e.g., legal, ethics, operations, end users) in AI governance processes to ensure balanced risk oversight and alignment with business goals.

Updated

A governance best practice where cross-functional teams (legal, compliance, data science, domain experts, and affected user representatives) participate in impact assessments, policy development, and review boards. This ensures that diverse perspectives shape AI decisions, align models with organizational values, and anticipate unintended consequences. Structured engagement includes workshops, surveys, and decision-logging for transparency.

Example

Before deploying an AI-driven hiring tool, HR, legal, ethics, and candidate-representative groups collaboratively review the model’s objectives, training data, and assessment criteria. Their joint feedback leads to adjustments that better protect candidate fairness and reduce legal exposure.

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.