AI Risk Management Framework Guidance

Introduction

The RAILS AI Risk Management Framework (RMF) Working Group brought together experts to develop a practical resource that empowers legal
professionals in corporate legal departments to integrate AI responsibly while maintaining public trust.

 

About This Resource  

This document is intended to provide corporate legal teams with practical guidance on how to establish, implement, and maintain an AI risk management framework (RMF) for their business. The public release of ChatGPT in 2022 opened the doors of generative AI and triggered a spectacular growth in AI-focused products, regulatory attention, and discourse in the media. The speed, size, and evolving nature of that growth, being fueled again with the emergence of Agentic AI, have presented corporate legal teams with a significant task as they seek to work with their clients to develop a robust approach to risk management concerning AI,.

In an era where AI technologies evolve at lightning speed, businesses face new regulatory landscapes and ethical considerations. The RAILS AI Policy Guidance Framework offers legal professionals a practical, comprehensive approach to managing AI-related risks in their organizations.

This resource is designed as a step-by-step “how-to” guide, not just a compliance checklist. It empowers legal teams to build, implement, and maintain an AI Risk Management Framework (RMF) tailored to their unique operational, legal, and technological contexts.

RAILS AI Risk Management Framework Guidance For Corporate Legal Teams. February 2025

Key Highlights

 

Foundational Principles

A flexible, industry-agnostic approach that supports innovation without compromising on ethical and regulatory standards.

  • Risk Understanding & Categorization –  Clear guidance on identifying and categorizing AI risks—ranging from data quality and transparency to regulatory compliance and ethical concerns.
  • Actionable Framework Building –  Practical tools for calibrating, prioritizing, and governing AI risks, supported by example use cases and strategic risk mitigation tactics.
  • Continuous Improvement –  Strategies to adapt and evolve your risk management processes in line with emerging technologies, regulations, and industry best practices.

Whether you are integrating AI into daily operations or managing high-stakes AI applications, the RAILS AI Policy Guidance Framework helps legal teams build a robust, future-ready approach to responsible AI use with the following steps.

AI Risk Management Process: Key Steps

  1. Understand Risks – Identify potential AI-related risks based on their causes and effects (e.g., human, operational, regulatory)
  2. Categorize and Calibrate – Classify risks using dimensions like data quality, transparency, security, and likelihood versus impact.
  3. Prioritize – Rank risks by importance, considering their alignment with business objectives and urgency of mitigation.
  4. Govern – Define roles, responsibilities, and reporting structures; establish oversight committees and accountability frameworks.
  5. Build Framework – Design a comprehensive RMF, incorporating existing structures and ensuring policy creation, incident response, and stakeholder engagement.
  6. Monitor – Track performance using Key Risk Indicators (KRIs) and dashboards; maintain oversight through audits and reporting systems.
  7. Mitigate – Implement safeguards, backup systems, data privacy measures, and response playbooks to minimize risk impact.
  8. Continuous Improvement – Conduct post-incident reviews, update policies, and benchmark against industry best practices to evolve and enhance the RMF.

Please use the RAILS AI Policy Guidance Framework to help foster the responsible use of AI with your legal services organization.

Establishing Risk Prioritization

Our framework defines prioritization as how to organize risks in order of importance or urgency for action.

The purpose of prioritization is to help the organization work out how it should concentrate its resources and efforts according to the calibrated risks.

Here is a suggested method of establishing risk prioritization within the RMF.

8 Step Method of Establishing Risk Prioritization. Clock shaped diagram featuring brief descriptions of each step.

Brief approaches tailored to different users of the RAILS AI Policy Guidance Framework

Goal – Gain a strategic understanding of AI risk management principles.

Approach – Focus on the Introduction and Principles sections to understand key challenges and high-level strategies for developing an AI Risk Management Framework (RMF). The Understanding AI Risks section offers essential insights into common risk categories and their impact, while the Continuous Improvement section outlines how to maintain resilience and adaptability in your approach. 

Application of the Guide – This guide will help you frame discussions with stakeholders, understand organizational readiness, and inform strategic decisions.

Goal – Lay a strong foundation for AI risk management.

Approach – Begin with the Understanding AI Risks section to familiarize yourself with common concerns like data privacy, transparency, and operational stability. Move to the Developing Your Framework section, which walks you through building an RMF from scratch, with practical steps for risk calibration, governance structures, and stakeholder alignment.

Application of the Guide – Follow the roadmap in the guide to methodically build a risk-aware culture and operational framework, avoiding common pitfalls.

Goal – Refine and align your risk management strategy for implementation.

Approach – Dive into the Developing Your Framework and Implementing Your Frameworksections, focusing on risk prioritization, governance roles, and incident response protocols. Use the example scenarios, risk identification questions, and suggested KPIs to strengthen your planning process. 

Application of the Guide – Ensure your planning addresses not only the legal requirements but also the operational, ethical, and technical complexities of your organization’s AI use cases.

Goal – Optimize and future-proof your existing risk management process.

Approach – Focus on the Continuous Improvement and Risk Monitoring sections to assess and improve your current framework. Leverage the guidance on incident reviews, audits, and reporting systems to identify gaps and adapt to evolving regulations and industry best practices.

Application of the Guide – Use this guide to enhance team collaboration, refine incident response protocols, and reinforce a culture of ongoing evaluation and improvement.

Send Feedback

We recognize RAILS members and colleagues may have additional insights, so please contact us at rails@law.duke.edu with any feedback.  

Copyright Information

The RAILS AI Risk Management Framework: Guidance for Corporate Legal Teams is licensed under Creative Commons Attribution-NonCommercial 4.0 International: CC BY-NC 4.0 

Read more about the license on our Resources page or through Creative Commons.