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

Metrics & KPIs

Metrics & KPIs

Metrics & KPIs

Quantitative measures (e.g., accuracy drift, fairness scores, incident response time) used to monitor AI system health, risk, and compliance objectives.

Quantitative measures (e.g., accuracy drift, fairness scores, incident response time) used to monitor AI system health, risk, and compliance objectives.

Updated

A dashboard-driven set of indicators: model-specific (accuracy, latency), governance-specific (percentage of models with current impact assessments), and organizational (mean time to bias remediation). Metrics are reviewed at defined cadences by governance bodies and linked to strategic goals, enabling data-driven decisions on resource allocation, process improvements, and risk prioritization.

Example

A tech company’s AI Governance Scorecard includes KPIs: 100% of production models have up-to-date validation, average incident response time under 24 hours, and monthly fairness-metric trends. The Data Ethics Council reviews these metrics quarterly to guide policy updates.

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.