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

Workload Segregation

Workload Segregation

Workload Segregation

Separating AI compute environments (e.g., dev, test, prod) and data domains to limit blast radius of failures or security breaches.

Separating AI compute environments (e.g., dev, test, prod) and data domains to limit blast radius of failures or security breaches.

Updated

The practice of isolating computational workloads, data stores, and network segments according to environment or classification level (development vs. production, PII vs. non-PII), enforced through network policies, distinct IAM roles, and separate clusters or namespaces. Governance defines environment boundaries, data-domain labels, and access controls, ensuring that a compromise or failure in one area does not propagate to critical systems or expose sensitive data.

Example

A cloud-based AI platform runs development workloads in a separate Kubernetes namespace with no access to production databases. Only approved release pipelines can promote container images to the production namespace, enforcing strict workload segregation and minimizing risk of accidental data 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.