GenAI will transform cyber risk management, creating new opportunities for attackers but also defenders. The good news is that, with Factor Analysis of Information Risk (FAIR), we have a flexible tool to confront whatever novel risk scenarios come at us – but we will need to pivot to meet the challenge.
Learn more in this blog post: What’s New about Generative AI Risk?
The FAIR Institute presents FAIR-AIR™, a FAIR-inspired approach to help you identify your AI-related loss exposure and make risk-based decisions on treating this new category in cyber risk management.
Read the Playbook to learn how to:
1. Recognize the five vectors of GenAI risk
2. Identify risk scenarios for analysis
3. Quantify the probable frequency and magnitude of AI-related cyber loss events
4. Prioritize and treat AI risks
5. Compare treatment options for a final decision
Example FAIR Analysis Output
“There is a 5% probability in the next year that Employees will leak company-sensitive information via an open-source LLM Model (like chat GPT), which would lead to $5 million dollars of losses.”
Watch FAIR Institute FAIRCON Session Videos:
How to Get Started with FAIR Analysis for GenAI Risk
-- FAIR Institute member Brandon Sloane of Meta and Pankaj Goyal, Research Director of the FAIR Institute, demonstrate setting up risk scenarios for quantitative analysis
The Good News on AI Risk – We Can Analyze It with FAIR
-- Tyler Britton and Taylor Maze tell how they launched FAIR-based risk management practices for GenAI at Dropbox.
The NIST AI RMF is a good starting point to wrap your head around the AI risk landscape. If you’re a FAIR practitioner used to thinking in terms of risk scenarios and probabilistic outcomes, you’re already in tune with NIST’s advice on managing artificial intelligence risk.
Watch a FAIR Institute Video: Intro to the NIST AI RMF - with Martin Stanley of NIST and CISA
Also see:
Together with our technical adviser Safe Security, the FAIR Institute created a GenAI Risk Platform that includes:
