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

Root Cause Analysis

Root Cause Analysis

Root Cause Analysis

A structured investigation to determine the underlying reasons for AI system failures or unexpected behaviors, guiding corrective actions.

A structured investigation to determine the underlying reasons for AI system failures or unexpected behaviors, guiding corrective actions.

Updated

A post-incident methodology (5 Whys, fishbone diagrams) that traces failure events (e.g., model misclassifications, system outages) back through data pipelines, model logic, and infrastructure layers to identify the primary defect. Root-cause analysis documents findings and remediation plans, ensures corrective actions address systemic issues, and prevents recurrence. Governance requires RCA for any high-severity incident, with executive review of outcomes.

Example

After a recommendation engine began suggesting inappropriate content, the team performed root-cause analysis: they found a data-ingestion script had merged test and production data, skewing training. They fixed the script, revalidated the model, and added unit tests to the ingestion pipeline, eliminating the risk of future cross-environment contamination.

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.