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NIST describes the AI RMF as a resource developed with the private and public sectors to manage risks to individuals, organizations and society associated with artificial intelligence, intended for voluntary use and aimed at incorporating trustworthiness into the design, development, use and evaluation of AI systems. Version 1.0 was released on 26 January 2023, and NIST has noted that it is being revised.

Its four functions give a workable agenda for a project. Govern establishes who is accountable and what the policies are. Map documents the context, the intended use and who could be affected. Measure decides how performance and risk will be assessed, with actual tests rather than impressions. Manage allocates resources to the risks that matter and sets out what happens when something goes wrong.

NIST also publishes a Playbook alongside the framework and, since 26 July 2024, a Generative AI Profile numbered NIST AI 600-1 that identifies risks unique to generative systems and suggests actions against them. Asking a consultant to structure their proposal against these functions is a fair test of whether they think in risk terms or in tool demonstrations.

A pilot should be scoped to a single workflow with a measurable current baseline: how long it takes today, how often it goes wrong today, what an error costs today. Without that baseline any result can be described as a success, which is precisely why so many pilots are.

Evaluation is the step that gets skipped. It means assembling a set of real examples with known correct answers, running the system against them, and recording how often it is right, how it fails when it fails, and whether the failures are the harmless kind. That test set is an asset: it is how you compare a new model version against the current one instead of guessing.

Production adds the unglamorous parts. Logging of inputs and outputs so a decision can be reconstructed later, monitoring for drift in quality, a defined owner, a documented fallback when the service is unavailable, and a retirement plan. Ask what the consultant hands over so your own staff can run all of that, and who is responsible for it after the engagement ends.

The US Copyright Office launched an initiative in early 2023 examining copyright law and policy questions raised by AI, including the scope of copyright in AI-generated works and the use of copyrighted material in AI training. It published a notice of inquiry in August 2023 that drew over ten thousand comments, and it is issuing a report in parts. Part 1, on digital replicas, was published on 31 July 2024. Part 2, on the copyrightability of outputs created using generative AI, was published on 29 January 2025. A pre-publication version of Part 3 was released on 9 May 2025.

The Office has also published registration guidance for works containing AI-generated materials, and a series of registration and Review Board decisions in individual cases. If you intend to register or commercially rely on material a system produced, read that guidance and take advice, because the position turns on facts about human contribution rather than on a general rule you can apply from a summary.

Practically, keep records. Ask your consultant what in each deliverable was machine-generated, what a person wrote or materially shaped, and what training or reference data was used and under what licence. If a vendor offers an indemnity for output-related claims, read what it actually covers and what it requires of you. None of this is legal advice; it is the paperwork your attorney will want to see.

Generative systems produce fluent output whether or not they have grounds for it, and fluency is a poor signal of accuracy. The failure mode that matters is a plausible, specific, wrong answer that a busy person accepts because it reads well. Design around that rather than hoping a better model removes it.

Human review should be targeted, not universal, or it will be abandoned within a month. Reserve it for outputs that reach a customer, that affect a person's money, employment, housing, credit or health, or that are hard to reverse. Low-stakes drafting can go unreviewed with a sampling check.

Give the reviewers something to work with. Citations back to source documents, confidence signals, and a one-click way to flag a bad output that actually feeds back into the test set. A review step with no record of what was rejected teaches the organization nothing and quietly becomes a rubber stamp.

Existing sector rules apply to AI systems the same way they apply to anything else. If a tool processes protected health information for a covered entity, HHS treats a vendor creating, receiving, maintaining or transmitting that information on the entity's behalf as a business associate, requiring a business associate agreement, and it treats cloud providers holding such data the same way. An AI vendor unwilling to sign one cannot be used for that workload.

If a firm handles customer financial information, the FTC Safeguards Rule's requirement to select service providers capable of maintaining appropriate safeguards and to bind them by contract covers an AI vendor as much as a hosting company, and the written information security program has to account for the new data flow.

For a public company, cybersecurity governance is now a disclosed item: the SEC requires annual description of cybersecurity risk management, strategy and governance under Regulation S-K Item 106, and material incidents on Form 8-K Item 1.05 generally within four business days of the materiality determination. A new vendor holding company data is part of that picture. Put the diligence in writing before the pilot, not after it becomes the production system.