Crosswalk pair

ISO 9001 and NIST AI Risk Management Framework, control by control

1 canonical control in Keel’s library satisfies clauses of both ISO 9001 and NIST AI Risk Management Framework. Implement each once, attach the evidence once, and it counts toward each standard. The overlap is the work you don’t repeat.

Start free with ISO 9001 + NIST AI Risk Management Framework See all pairs

The overlap

What the two libraries have in common

Every figure here counts canonical controls in Keel’s library, not clauses of either standard. Each standard’s own authored count is on its framework page.

1

Controls that satisfy both

Canonical controls that crosswalk to at least one clause of each.

43

In Keel’s library for ISO 9001

2% of them also map to NIST AI Risk Management Framework.

27

In Keel’s library for NIST AI Risk Management Framework

4% of them also map to ISO 9001.

3

Evidence artifacts expected

Across the shared controls, from Keel’s evidence guidance. Gathered once.

ISO 9001 2015 2%

1 control of 43 in Keel’s library for ISO 9001 also maps to NIST AI Risk Management Framework.
NIST AI Risk Management Framework 1.0 4%

1 control of 27 in Keel’s library for NIST AI Risk Management Framework also maps to ISO 9001.

The mapping

Controls that satisfy both

Each row is one control in Keel’s library and the clauses it answers on each side. Do the work once; both columns are then evidenced by the same artifacts.

ISO 9001 and NIST AI Risk Management Framework controls that satisfy both, with the clauses each maps to
Canonical control	ISO 9001 clauses	NIST AI Risk Management Framework clauses
Continual improvement Improvement of the management system is run as an activity with a record, not held as an intention. Opportunities are captured from everywhere they arise - audit findings, the results of measurement and evaluation, decisions out of management reviews, incidents and near misses, and suggestions from the people actually doing the work - and held in one place instead of in the meeting each came out of. Each is evaluated and either taken forward with an owner and a date or closed with the reason it was not, so a rejected idea is a decision rather than a silence. Once an improvement is made, its effect on the suitability, adequacy and effectiveness of the system is checked, so improvement is something that can be shown to have happened rather than asserted at the next audit. The routine execution of the work counts as a source in its own right: what the operational processes, procedures and activities themselves show while they are being run - the step that is always skipped, the check that never fires, the manual workaround everybody has quietly adopted - is captured on the same terms as a finding from an audit, because the people running a process daily see more of it than any evaluation does. Where the thing being improved is an AI system, the improvement activity is built into the system’s own update cycle and is measurable: an update carries the improvement it is meant to deliver and the measure that will show whether it did, checked afterwards rather than asserted, and interested parties - the people who operate the system, the people it is used on, and those who represent them - are engaged regularly as part of that cycle rather than consulted once at launch. The opportunities are not confined to the system: what the organization delivers is in scope too. Improving products and services to meet known requirements and to address needs and expectations that are coming rather than current, correcting, preventing or reducing undesired effects, and improving how the system itself performs are all determined and selected against as improvement opportunities, rather than run as three unrelated programs. And the standing question - what in the system’s suitability, adequacy and effectiveness should be improved next - is answered from evidence rather than appetite: the results of analysis and evaluation and the outputs of management review are considered together to decide whether there is a need or an opportunity that has to be addressed.	10.1, 10.3	MANAGE-4.2

Beyond the pair

Where else this work counts

A framework is lit when a shared control above also maps to it. Unlit means none of them do — an absence, not a judgment about that standard.

Also reached by this control

AI Governance Essentials
Amazon Appstore Child-Directed Apps
Apple App Store Kids Category
CIS Critical Security Controls
COPPA
ESG Essentials
EU AI Act
GDPR
Google Play Families
HIPAA
ISO/IEC 27001
ISO/IEC 42001
NIST Cybersecurity Framework
NIST SP 800-171
NIST SP 800-53
PCI DSS
SOC 2
SOX (Sarbanes-Oxley) Section 404
US Employment Law - Federal Baseline

Nearby pairs

The thesis

Why this is one project, not two

On a crosswalk-native model, NIST AI Risk Management Framework mostly lights up controls you already built for ISO 9001. You’re not re-uploading the same screenshot for a second audit. You apply the framework and see the genuine delta worth working. That’s the whole idea behind collect once, comply everywhere.

Next step

Add NIST AI Risk Management Framework to the work you already did

Apply both frameworks in one workspace and see the overlap measured against the controls you already hold.

Start free with ISO 9001 + NIST AI Risk Management Framework See all pairs