What evidence do auditors commonly ask for on MN-2.2 Mechanisms are in place and applied to sustain the value of deployed AI systems?

The mechanisms in place to sustain value, such as retraining or recalibration; Records showing those mechanisms were applied, with dates; The trigger conditions that cause them to be applied; Evidence of the effect on performance after application.

What is the most common gap auditors find on MN-2.2?

Retraining capability exists but has not been exercised since deployment

NIST AI RMF 1.0 · MANAGE - NIST AI RMF 1.0

`MN-2.2` Mechanisms are in place and applied to sustain the value of deployed AI systems

Mechanisms are in place and applied to sustain the value of deployed AI systems. There are applied mechanisms, retraining, recalibration or refresh, that maintain the system's value against drift, and evidence they are used rather than merely available.

Source: NIST AI RMF 1.0, MN-2.2. The statement above is our statement of the clause, read from the published text and cited so you can check it against the official text, not the instrument verbatim. Open NIST AI RMF 1.0 in the compliance library.

artefacts

held by a system

at each review

hard

to go live

Policy repository / GRC workspace

where the evidence lives

teal = a system already holds it · olive = produced at each review. To go live: easy when half or more of the artefacts are already held by a system, moderate from a quarter to a half, hard below a quarter.

These are the artefacts auditors commonly ask for on this requirement, our guidance rather than text from the standard. Your assessor decides what is sufficient in your context, and asks for samples across the period when a report covers one.

system holds itEvidence a system already holds

none for this control

periodic reviewEvidence produced at each review

Records showing those mechanisms were applied, with dates · Policy repository / GRC workspace
Evidence of the effect on performance after application · Policy repository / GRC workspace

governing documentDocuments that govern the control

The mechanisms in place to sustain value, such as retraining or recalibration · Policy repository / GRC workspace
The trigger conditions that cause them to be applied · Policy repository / GRC workspace

First move

No system holds this evidence for you: it is people and documents. Reminders and routing to the owner still save time. Put the document under version control with an owner and review date, and log each review as a record with reviewer and date.

Common gaps auditors find

Retraining capability exists but has not been exercised since deployment
Triggers defined against calendar time rather than observed degradation
Effect of retraining unmeasured, so sustainment is assumed

Do this for your whole sheet

Paste the rows you run your controls from and get this mapping for every control at once, with the periodic-review ones flagged and a first move per row. No account for the first run.

Build my evidence sheet

MN-2.1 Resources required to manage AI risks are taken into account, along with viable non-AI alternative systems… · MN-2.3 Procedures are followed to respond to and recover from a previously unknown risk when it is identified