← All posts

AI Risk Treatment Decisions: Deciding to deploy, limit or decline

August 10, 2026 · Ken Armstrong

NIST AI RMF MANAGE 1.1 is the outcome on AI risk treatment decisions. It is short to read and easy to underestimate, because satisfying it means producing evidence rather than holding an intention.

What the framework says

A determination is made as to whether the AI system achieves its intended purposes and stated objectives and whether its development or deployment should proceed.

That is the text of NIST AI RMF MANAGE 1.1. Read it closely: it describes an outcome to achieve, not a product to buy, which is why two practices of the same size can satisfy it in different ways and both be right.

What this looks like in practice

In an assessment, NIST AI RMF MANAGE 1.1 is not one question. It resolves into several, and each one is really asking for different evidence. These are among the questions asked about it:

  • Has it been determined whether this AI system achieves its intended purpose and stated objectives?
  • Is there a documented, accountable decision on whether this AI system's development or deployment should proceed?
  • Is there a documented deployment plan for this AI system, covering the target environment, rollout steps, rollback and a go/no-go decision by an accountable person?

Ownership usually sits with the product owner, though the evidence is often produced by someone else, which is where the trail breaks. A control that works but has no owner tends to stop working the month the person who quietly maintained it changes roles.

Who this applies to

NIST AI RMF is voluntary rather than binding, and that changes how it should be used. Adopting it does not create a legal obligation, and declining it is not a violation. It earns its place when a customer contract names it, when it gives structure to AI governance work you already owe under a law or a contract, or when a recognized practice is worth having on record. Treat it as a way of organizing that work, not as a second regime.

Building it, in order

The sequence below is the order that produces evidence as a by-product rather than as a separate documentation exercise:

  1. Compare evaluation results with the system's stated objectives.
  2. Record where objectives are met, partly met or not met.
  3. Obtain the business owner's conclusion.
  4. Present the system's risk and performance findings to the decision-maker its risk tier requires (executive leadership for high-risk systems).
  5. Record a go, conditional-go or no-go decision with its rationale.
  6. Revisit the decision on material change.

Where else this maps

The questions behind NIST AI RMF MANAGE 1.1 also map to ISO/IEC 42001 A.6.2.5, which is voluntary guidance and standards rather than law; following it does not by itself establish compliance with any law. That matters for scoping: the same evidence can be written once and referenced from each, rather than rewritten per framework.

Evidence an assessor asks for

For this outcome the evidence types that satisfy it are plan, record, and report. The distinction matters more than it looks: a policy states what you intend to do, a procedure states how, and a record proves it happened on a date. Auditors ask for all three, and a practice that has written the first two often has nothing for the third.

Date every document and keep the superseded versions. The framework sets no retention period, so this is our recommendation rather than a requirement: keep each version for as long as the system it describes is in use and for a period after it is retired, because a question about a past decision is answered from the version that was in force when it was made.

Where this usually goes wrong

The usual gap is a control that exists in practice and nowhere in writing. Someone does the work, reliably, and it has never been written down, so the organization cannot demonstrate it and cannot notice when it stops. The fix is not more control, it is a dated record and a named owner.

What to do about it

  • Produce the evidence, not the intention. For NIST AI RMF MANAGE 1.1 that means a written plan naming who does what, and evidence it was tested, dated records of each occurrence, not a description of the process, and a dated report with the period it covers and who reviewed it. If the only evidence is that someone knows how this works, you have a practice and not a record.
  • Put a name on it. The product owner should be recorded as the owner. Where the work is done by someone else, say so in the document: the split between who owns it and who performs it is where the trail usually breaks. A control with no owner stops working the month the person who quietly maintained it changes roles, and nobody notices.
  • Re-read it when something changes. A new system, a new vendor or a departure is what makes a correct control stale. Tie the review to those events as well as to the calendar, and date the result.
  • Do not treat this as a legal obligation. It is a voluntary framework. Adopt it where a customer contract or a regulator's guidance names it, or where it gives structure to AI governance work you owe for other reasons, and say in your own documentation which of those reasons applies.

Work backward from the evidence. Decide what evidence would prove AI risk treatment decisions to someone who does not already trust you, then build the control that produces it. Programs built in that order tend to stay current, because the evidence goes stale visibly.

Related outcomes

References

  • MANAGE 1.1, in NIST AI Risk Management Framework 1.0 (NIST AI 100-1). This is a voluntary framework, not a legal requirement. The quoted statement is NIST's own, from the Core tables of NIST AI 100-1.
  • Crosswalk references in this post are the mappings in our question bank, and are not verified against the published text of those standards.
  • This post describes what a framework outcome asks for and what evidence documents it. It is not legal advice, and your counsel owns the conclusion. State law may impose additional obligations of its own.