AI governance designed for the way insurance leaders do business.

Controlled and validated AI governance, integrated across the
systems and software your teams already use.

AI governance shouldn't be just another disparate system. It belongs in the context of your business systems.
Monitaur connects validated governance to the modeling, code, workflow, and documentation systems your
teams work in every day, so AI risk gets managed with the same rigor you apply to every other material risk.

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AI governance shouldn’t sit in a silo

For most insurance carriers, AI has emerged as a disconnected discipline, separate from how the rest of the business is governed and managed. That separation is breaking down. Generative AI is moving into core insurance processes. Model volume is climbing. Regulators are sharpening expectations. Boards are asking sharper questions. 

The answer isn't a parallel governance system for AI. It's an integrated design that brings AI into the rigor your business already runs on. 

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A complete design for AI governance

Four phases that mirror how your business already operates. Bring Monitaur in where you need it most

Phase 1

Establishing a governance foundation

What happens here: Defining what good looks like for AI in your business. Policies, taxonomy, organizational ownership, alignment to frameworks like the NAIC Model Bulletin and NIST AI RMF.

How Monitaur supports this: A validated controls library gives you a foundation to build from. Map controls to the policies and frameworks your business already operates against, customize where it matters, and put structure under your AI governance practice without starting from scratch.

Connects to: Confluence, ServiceNow

Phase 2

Project intake and risk assessment

What happens here: Every AI project, model, and vendor system enters governance with a clear classification, risk tier, and pathway. The appropriate oversight gets applied at the right level, without slowing your teams down.

How Monitaur supports this: Structured intake captures every AI use case in one place. Risk tiering determines the depth of review. Workflows route projects to the right reviewers, with full visibility for risk and compliance leaders.

Connects to: ServiceNow, Confluence

Phase 3

Control implementation and automation

What happens here: Controls move from policy documents into live systems. Evidence flows automatically from where models are built, validated, and deployed

How Monitaur supports this: Pre-built integrations capture evidence from your modeling and development environments, so your AI teams keep working in the tools they already use. Validated controls run continuously. Documentation gets generated as the work happens, not after the fact.

Connects to: Databricks, MLflow, DataRobot, Azure, Amazon SageMaker, Amazon Bedrock, GitHub, LangFuse

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Connect to the systems your teams already use

Pre-built integrations across your modeling, development, workflow, and documentation stack.

Modeling and AI development

Databricks

MLflow

Data Robot

Azure

Amazon

SageMaker

Amazon Bedrock

Workflow and ITSM

ServiceNow

Code

GitHub

LLM observability

LangFuse

Documentation

Confluence

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Start where you want

Few carriers adopt every phase of AI governance at once. Most start where they feel the most pressure, whether that's intake discipline, control automation, or risk reporting, and expand from there. Monitaur is designed to plug into the phase where you need it most, with a clear path to expand as your AI governance practice matures. 

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Extend the model risk discipline you already trust

Insurance carriers have spent decades building rigorous model risk management practices. AI governance doesn't replace that discipline. It extends it. Monitaur applies the same standard of validation, control, and oversight to the broader set of AI systems shaping your business, from traditional models to generative AI. 

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See where Monitaur can help with AI governance

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