Standard corporate policies regarding the use of "Opt-out" mechanisms for third-party Generative AI training are sueicient to guarantee full…

The statement is false. The NIST AI Risk Management Framework (RMF) is a comprehensive, voluntary framework addressing the full lifecycle of AI risks, including fairness, accountability, transparency, safety, and security—not just data privacy. An "opt-out" mechanism is a single, narrow control related to data usage for training. Relying solely on this mechanism is insufficient to address the broad spectrum of trustworthiness characteristics and governance functions detailed in the NIST AI RMF.

True: This is incorrect because a single data privacy control like an "opt-out" mechanism cannot possibly satisfy the holistic requirements of the NIST AI RMF, which covers governance, risk mapping, measurement, and management across multiple facets of AI trustworthiness.

1. National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). The framework's core, described in Chapter 3, consists of four functions: GOVERN, MAP, MEASURE, and MANAGE. Full alignment requires addressing all functions, which extend far beyond a single data privacy control.

2. The White House. (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Section 4.1 calls for guidance on managing AI risks consistent with the NIST AI RMF, emphasizing a broad scope including safety, security, bias, and transparency, confirming that single controls are insufficient.

3. MIT Schwarzman College of Computing. (2023). AI Policy and Governance. Course materials discuss how comprehensive governance frameworks like the NIST AI RMF require a multi-faceted approach, including organizational policies, technical tools, and continuous monitoring, not just isolated user consent mechanisms.