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

Tail Risk

Tail Risk

Tail Risk

The potential for rare, extreme outcomes in AI behavior or decision-making that fall outside normal expectations and require special mitigation planning.

The potential for rare, extreme outcomes in AI behavior or decision-making that fall outside normal expectations and require special mitigation planning.

Updated

Focuses on low-probability but high-impact events: catastrophic model failures, adversarial exploits, or emergent behavior in complex systems. Tail-risk governance includes stress-testing models on extreme scenarios, defining contingency plans (kill switches, fail-safe modes), and maintaining reserves (financial, operational) to absorb shocks. Risk metrics (e.g., Value at Risk) are extended to cover AI-driven decision contexts.

Example

An autonomous-drone inspection service simulates rare failure modes (GPS spoofing, extreme weather) and verifies that the system safely lands rather than continues unstable flight. The company’s tail-risk plan includes emergency-grounding procedures and redundant communication links to minimize potential harm.

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.