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

Post-Deployment Monitoring

Post-Deployment Monitoring

Post-Deployment Monitoring

Ongoing observation of AI system behavior and environment after release to detect degradation, drift, or compliance breaches.

Ongoing observation of AI system behavior and environment after release to detect degradation, drift, or compliance breaches.

Updated

Extends model monitoring to include governance signals (privacy incidents, policy-violation logs, ethics-metric trends) alongside performance and security metrics. Post-deployment monitoring frameworks ingest diverse telemetry, run periodic audits (e.g., fairness checks, anomaly scans), and trigger governance workflows when thresholds are crossed, ensuring corrective actions (retraining, rollback, legal notification) occur promptly.

Example

A social-media platform’s hate-speech detector sends all content flagged as high-risk or unclassified by the AI into a moderator queue. Post-deployment, the system monitors false-positive rates monthly; if the false-positive rate exceeds 2%, the governance dashboard opens an investigation ticket for model retraining and rule adjustments.

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.