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Safety cases, assurance plans, and documented evidence of safety claims.
Also in Risk & Assurance
Reasoning
Documents measurement results of AI trustworthiness informed by domain experts to validate deployment performance—assurance documentation supporting safety claims.
Conduct adversarial testing at a regular cadence to map and measure GAI risks, including tests to address attempts to deceive or manipulate the application of provenance techniques or other misuses. Identify vulnerabilities and understand potential misuse scenarios and unintended outputs.
2.2.2 Testing & EvaluationEvaluate GAI system performance in real-world scenarios to observe its behavior in practical environments and reveal issues that might not surface in controlled and optimized testing environments.
2.2.2 Testing & EvaluationImplement interpretability and explainability methods to evaluate GAI system decisions and verify alignment with intended purpose.
2.2.2 Testing & EvaluationMonitor and document instances where human operators or other systems override the GAI's decisions. Evaluate these cases to understand if the overrides are linked to issues related to content provenance.
2.3.3 Monitoring & LoggingVerify and document the incorporation of results of structured public feedback exercises into design, implementation, deployment approval (“go”/“no-go” decisions), monitoring, and decommission decisions.
2.2.4 Assurance DocumentationLegal and regulatory requirements involving AI are understood, managed, and documented.
2.1.3 Policies & ProceduresLegal and regulatory requirements involving AI are understood, managed, and documented. > Align GAI development and use with applicable laws and regulations, including those related to data privacy, copyright and intellectual property law.
2.1.3 Policies & ProceduresThe characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.
2.1.3 Policies & ProceduresThe characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. > Establish transparency policies and processes for documenting the origin and history of training data and generated data for GAI applications to advance digital content transparency, while balancing the proprietary nature of training approaches.
2.1.3 Policies & ProceduresThe characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. > Establish policies to evaluate risk-relevant capabilities of GAI and robustness of safety measures, both prior to deployment and on an ongoing basis, through internal and external evaluations.
2.1.3 Policies & ProceduresProcesses, procedures, and practices are in place to determine the needed level of risk management activities based on the organization’s risk tolerance.
2.1.3 Policies & ProceduresArtificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1)
US National Institute of Standards and Technology (NIST) (2024)
This document is a cross-sectoral profile of and companion resource for the AI Risk Management Framework (AI RMF 1.0) for Generative AI, 1 pursuant to President Biden’s Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence.2 The AI RMF was released in January 2023, and is intended for voluntary use and to improve the ability of organizations to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.
Other (multiple stages)
Applies across multiple lifecycle stages
Deployer
Entity that integrates and deploys the AI system for end users
Measure
Quantifying, testing, and monitoring identified AI risks
