GenAI Risk Management: A Layman's Guide to Responsible AI Use

This is part of the Generative AI (GenAI) risk management framework blog series. Refer to the individual tabs to learn how you can effectively manage GenAI risks at different stages of the GenAI lifecycle within your organization.

Generative AI (GAI) is an exciting new technology with the power to create text, images, and even entire virtual environments. However, as with all powerful tools, it comes with risks. To use it responsibly, organizations need to manage these risks carefully. The NIST AI 600-1 framework provides useful advice on how to do this. This guide explains the suggested actions under the “Govern” section, focusing on how organizations can govern AI risks effectively.

GenAI Risk Management : GOVERN

1. Understand the Legal and Regulatory Rules

Key Idea: Organizations using AI must follow the laws that apply to data privacy, intellectual property, and discrimination. This means making sure that AI systems respect people’s privacy and don’t use copyrighted material without permission.

What You Can Do: Review the laws that apply to your AI project and ensure everything is legal. For example, if your AI uses personal data, make sure that data was collected with permission and follows privacy laws.

2. Build Trust in AI

Key Idea: People need to trust AI systems. To build that trust, organizations must be transparent about how their AI works and where the data comes from.

What You Can Do: Create clear documentation that explains how your AI is trained, what data it uses, and how it works. This makes your AI more trustworthy because people can see how decisions are made.

3. Define Your Risk Levels

Key Idea: Not all risks are equally important. Organizations need to decide which risks they are willing to take and which ones they want to avoid at all costs. This helps them focus on the most important problems.

What You Can Do: Set clear standards for your AI system. For example, you might decide that the AI can’t be deployed if it makes too many mistakes or generates harmful content.

4. Be Transparent About How You Manage Risks

Key Idea: Transparency is key. Organizations need clear processes to manage risks, and they must be upfront about how they plan to prevent AI from being misused.

What You Can Do: Establish policies that explain what your AI can and cannot do, and make sure these policies are shared with everyone involved. Regularly review these policies to ensure your AI stays within legal and ethical boundaries.

5. Keep Monitoring Your AI

Key Idea: AI risks change over time. To stay on top of these risks, organizations need to constantly monitor their AI systems and review the risk management process regularly.

What You Can Do: Assign specific people to monitor your AI systems and check regularly for issues like data leaks or harmful outputs. This ongoing review helps catch problems before they get out of hand.

6. Track Your AI Systems

Key Idea: Organizations should keep a detailed inventory of their AI systems. This helps track how each system is performing and ensures that resources are being used wisely.

What You Can Do: Keep records of all the AI systems you use, including what data they rely on and any known issues. This way, you can quickly address any problems and allocate resources to manage risks more effectively.

7. Safely Shut Down AI Systems

Key Idea: If an AI system is no longer needed or poses too much risk, it should be safely deactivated to avoid any negative consequences.

What You Can Do: Create protocols to ensure that AI systems can be turned off safely. Make sure that shutting down the AI won’t cause issues with connected systems or expose any sensitive data.

8. Clarify Roles and Communication

Key Idea: For effective risk management, organizations must clearly define who is responsible for what and how they will communicate about AI risks.

What You Can Do: Set up clear communication channels and make sure every team member understands their role in managing AI risks. This makes it easier to address problems quickly and efficiently.

9. Manage Human-AI Interactions

Key Idea: When humans and AI interact, things can go wrong. Organizations need to ensure that human oversight is strong and that AI systems are evaluated independently to check for mistakes.

What You Can Do: Put policies in place for regular evaluations of your AI systems by independent teams. This helps catch errors or biases that might not be obvious in day-to-day use.

10. Focus on Safety in AI Design

Key Idea: Safety should always come first. Organizations should foster a culture of safety in their teams and constantly improve how they measure risks in AI systems.

What You Can Do: Make risk assessments a regular part of your AI development process. Use tools like red-teaming (simulating attacks on your AI) or independent audits to find weaknesses and fix them.

11. Communicate AI’s Impacts Clearly

Key Idea: Organizations should be upfront about how their AI systems affect users and stakeholders. This includes communicating potential risks and documenting how AI is used.

What You Can Do: Create clear terms of use for your AI systems and make sure users know how the AI works, what data it uses, and what the potential risks are.

12. Test, Report, and Share Information

Key Idea: Testing your AI system regularly helps identify issues early. You should also have procedures in place to report and share information about incidents.

What You Can Do: Set up an incident reporting system to document issues like data leaks or security breaches. Share this information with your team and external stakeholders to improve transparency.

13. Gather External Feedback

Key Idea: External feedback is valuable for improving AI systems. Organizations should collect feedback from outside parties and use it to improve their risk management strategies.

What You Can Do: Create feedback mechanisms that allow users to report issues or suggest improvements. Use this feedback to adjust your risk management strategies and make your AI more reliable.

14. Manage Third-Party AI Risks

Key Idea: Many AI systems use third-party components, such as datasets or pre-built models. These external elements introduce additional risks that organizations must manage carefully.

What You Can Do: Assess the quality of third-party components before integrating them into your AI system. Make sure to use trustworthy data and check for any potential issues before deploying the system.

15. Be Ready for Third-Party Failures

Key Idea: If something goes wrong with a third-party component in your AI system, you need to be ready to handle the situation. This includes documenting failures and creating a response plan.

What You Can Do: Develop a contingency plan for dealing with third-party failures. This includes clearly defining who is responsible for fixing the issue and communicating with stakeholders when problems arise.

Conclusion

Generative AI brings incredible opportunities, but with it comes significant responsibility. By following the steps outlined in the NIST AI 600-1 framework, organizations can effectively manage the risks of using this powerful technology. The key is to stay proactive, transparent, and prepared for the unique challenges AI presents.

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