Monitaur Blog

Original insights and perspectives about model governance and risk management

What's new in model governance and risk?

Three common challenges with implementing AI for claims

Is AI adoption slowing down in your organization? Claims teams can benefit from effective uses of AI, but if trust isn't built into the changes, investments fall flat. Learn how AI governance can be an enabler of your AI strategy.

Monitaur in the Media

Impact & Society

Risks & Liability

Regulation & Legislation

Principles & Frameworks

Ethics & Responsibility

AI Governance & Assurance

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Life without AI governance: A wake-up call for customer-obsessed organizations

Get help with AI governance. Organizations that invest in great AI governance and align AI literacy across organizations are better equipped to use AI successfully while reducing risks.

Establishing the business case for AI governance in 2025

Regulation is a mere fraction of why AI governance matters. When approached comprehensively, governance programs accelerate AI investments to achieve business goals while managing AI risks and ensuring the company uses its resources to succeed.

Top 5 governance considerations for Agentic AI

Does every AI agent need to be built with an LLM for reasoning? No. Explore AI agent use cases and why agentic AI governance is mandatory for successful AI agent implementation.

CIO chat: What insurers need to know about AI governance

Learn how AI helps insurance companies, the importance of having a governance framework for AI, the role of CIOs in AI governance, and the future of AI in the insurance industry.

Scale model documentation the right way for AI governance

Learn how to scale model documentation methods for AI and complex modeling systems. Avoid the common mistakes that teams make early on.

AI governance for foundation models and generative AI

Explore governance of foundation models and generative AI products. Learn about evaluating and governing these technologies in third-party management and model risk contexts.

Preventing AI incidents, Part 1: Why robustness and resilience are critical for AI governance

In the aftermath of a worldwide IT incident, what can we learn about how to properly build and govern robust, performant, resilient AI systems, the right way.

How to succeed with AI projects

What should you consider when teams tell you they’ve got AI “covered”? To increase your chances of success with AI, align your organization and projects around the following actions.

Information theory: Not the best approach for model monitoring

Learn about the goals of information theory, define the differences between metrics and divergences, explain why divergences are the wrong choice for monitoring, and propose better alternatives.