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) systems are powerful tools that bring significant potential but also unique risks. The NIST AI 600-1 framework provides essential guidance on how to manage these risks effectively. The MANAGE section outlines key strategies to help organizations monitor, control, and respond to risks during the AI lifecycle. Below is a breakdown of how to implement these strategies based on the framework.
GenAI Risk Management : MANAGE
1. Responding to High-Priority Risks
Key Idea: When significant risks are identified, it’s important to address them promptly. Organizations can consider releasing AI systems in stages to reduce risk exposure.
What You Can Do: Implement a phased rollout of your AI system, using early feedback to monitor for potential risks. By releasing the system incrementally, you can catch and address any issues before a full-scale launch.
2. Monitoring AI-Generated Content
Key Idea: AI-generated content needs to be continuously tested to ensure it meets guidelines and doesn’t generate harmful, abusive, or biased outputs.
What You Can Do: Establish clear principles to assess AI-generated content. Regularly test for abusive or dangerous content and document the origins of the AI’s training data to maintain traceability. This allows you to track any bias or misuse in the system.
3. Preparing for Unforeseen Risks
Key Idea: Some risks only become apparent after the AI system is deployed. Organizations need to be ready to detect and manage these risks as they emerge.
What You Can Do: Update policies and procedures to detect new risks and include recovery plans. Make sure these plans clearly communicate how to address and fix issues that arise in the AI’s value chain.
4. Decommissioning AI Systems Safely
Key Idea: There may be instances where AI systems need to be deactivated. It’s essential to have clear procedures for safely shutting down systems when needed.
What You Can Do: Create protocols for safely decommissioning AI systems, ensuring all components—particularly sensitive data—are handled appropriately. Make sure that users are informed about the decommissioning process.
5. Managing Third-Party AI Resources
Key Idea: Many AI systems rely on third-party models or datasets, which introduce additional risks that need to be managed.
What You Can Do: Ensure that third-party models and data are well-documented and meet the required legal and ethical standards. Implement security checks on third-party resources to prevent potential issues related to intellectual property or data privacy.
6. Maintenance and Updates
Key Idea: AI systems require regular maintenance to ensure they remain effective and secure. This includes managing third-party model updates.
What You Can Do: Plan for regular system updates, particularly when using third-party models or datasets. Implement checks to ensure that these updates do not introduce new risks, such as vulnerabilities or biases.
7. Post-Deployment Monitoring
Key Idea: Once the AI system is deployed, continuous monitoring is essential to track how it is performing and to identify any risks that may arise.
What You Can Do: Set up a post-deployment monitoring system that gathers feedback from users and stakeholders. Use this feedback to make adjustments and improve the AI’s transparency and accountability. Monitoring should also include content provenance to ensure it complies with data security standards.
8. Reporting and Handling Errors
Key Idea: When incidents or errors occur, organizations need a clear process for reporting them and taking corrective action.
What You Can Do: Create a system to report incidents and errors in your AI systems. This system should also communicate with relevant legal and regulatory bodies when necessary. Make sure recovery processes are in place to minimize the impact of any issues and ensure compliance with laws and regulations.
Conclusion
Managing the risks associated with Generative AI requires an ongoing, structured approach. By following the guidelines in the NIST AI 600-1 framework, organizations can ensure they respond effectively to high-priority risks, maintain control over third-party resources, and monitor AI systems for unexpected issues. With a strong focus on post-deployment monitoring and proactive risk management, organizations can deploy GAI systems safely and responsibly.
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