AI Risk Assessment Framework

Introduction

AI implementations bring transformative potential but also significant risks. This framework provides a structured approach to identify, assess, mitigate, and monitor risks associated with AI systems. Designed for risk managers, AI leaders, and compliance teams, this guide ensures safety, compliance, and business continuity.

Key Insights

• Risk identification is the cornerstone of AI safety.
• Assessment methodologies help quantify risks effectively.
• Mitigation strategies must be proactive and adaptive.
• Continuous monitoring ensures compliance and reduces long-term exposure.

Framework Overview

This framework follows a four-phase approach: preparation, assessment, mitigation, and monitoring. Using risk management tools and templates, it ensures comprehensive coverage of all potential risk areas. The framework typically requires 3-4 weeks to implement and involves cross-functional collaboration.

Action Items

1. Identify risks using brainstorming sessions and risk templates.
2. Assess risk impact and probability using scoring methodologies.
3. Develop mitigation strategies and allocate resources accordingly.
4. Monitor risks using continuous tracking systems and update assessments periodically.