# AI Risk Management in Practice: A Census of 592 NIST AI RMF Playbook Actions

> A dated census of 592 actions across all 72 NIST AI RMF Playbook subcategories maps operational density, themes and a practical evidence packet.

- Canonical page: https://mtfinstitute.com/insights/ai-risk-management-592-nist-ai-rmf-playbook-actions/
- Content type: Article
- Editorial category: Research & Reports
- Publisher: MTF Institute of Management, Technology and Finance
- Author: MTF Institute Editorial Team- Published: 2026-09-07
- Updated: 2026-09-07
- Language: English
- Topics: AI governance, Responsible AI, AI Risk Management, NIST AI RMF

NIST presents its AI Risk Management Framework Playbook as a set of suggested actions, not a checklist. How large and operational is that action space? MTF Institute counted **592 visible suggested-action bullets** across all 72 public Playbook subcategories on 7 September 2026 and analysed their distribution, action orientation and recurring themes.

## Executive finding

AI risk management in the Playbook is strongly evidence-intensive. **MEASURE contains 239 of 592 actions (40.4%)**, more than any other function. In transparent, non-exclusive keyword coding, measurement, testing and monitoring language matched 257 actions; risk, impact and tolerance matched 216; and human or stakeholder participation matched 155.

| Function | Subcategories | Visible actions | Share |
|---|---:|---:|---:|
| GOVERN | 20 | 152 | 25.7% |
| MAP | 17 | 118 | 19.9% |
| MEASURE | 22 | 239 | 40.4% |
| MANAGE | 13 | 83 | 14.0% |
| **Total** | **72** | **592** | **100%** |

The result does not mean MEASURE is more important. It shows that the public Playbook gives the densest menu of actions to evaluation and measurement. A policy-only governance model therefore omits much of the practical workload.

## Research question

How is practical AI risk-management work distributed across the NIST AI RMF Playbook, which operational themes recur, and what minimum evidence packet can learners and managers build from the pattern?

## Scope and method

The study covers the four public Playbook function pages - GOVERN, MAP, MEASURE and MANAGE - and all 72 subcategories visible on 7 September 2026. Every subcategory was expanded in the Codex in-app browser.

The unit of analysis was one visible bullet under “Suggested Actions”. Nested bullets were counted separately when the page presented them as distinct actionable units. The resulting census contained 592 observations. For every subcategory, the action count was recorded. Across the 72 records, the minimum was 1, the median 7, the mean 8.22 and the maximum 36.

The study normalized the first alphabetic token of each action to describe opening verbs. It also applied a predeclared set of case-insensitive keyword and phrase patterns. Theme flags were non-exclusive: one action could match more than one theme, so thematic totals must not be summed to 592.

The supporting workbook publishes the 72-row sampling frame, function totals, thematic aggregates, opening-verb counts, method and limitations. Official NIST action text remains at the primary source and is not redistributed in bulk.

## Where the Playbook is most detailed

| Subcategory | Visible actions | Operational emphasis |
|---|---:|---|
| MEASURE 2.11 | 36 | Fairness and bias |
| MEASURE 2.5 | 24 | Validity and reliability |
| MAP 2.3 | 21 | Scientific integrity and TEVV |
| GOVERN 1.4 | 20 | Transparent process and documentation |
| GOVERN 2.1 | 18 | Roles and communication |
| MAP 1.1 | 17 | Context and intended use |
| GOVERN 1.2 | 15 | Trustworthiness in policy and process |
| MEASURE 2.2 | 14 | Human-subject representativeness |
| MANAGE 2.1 | 14 | Resources and non-AI alternatives |
| MEASURE 2.8 | 13 | Transparency and accountability |

Action density is descriptive. More bullets do not prove greater importance, effort or maturity. It does show which subcategories a practitioner should expect to decompose into several distinct records or tests.

## Recurring operational themes

| Non-exclusive theme | Matched actions | Share of 592 |
|---|---:|---:|
| Measurement, testing and monitoring | 257 | 43.4% |
| Risk, impact and tolerance | 216 | 36.5% |
| Human and stakeholder participation | 155 | 26.2% |
| Documentation, evidence and traceability | 115 | 19.4% |
| Governance, policy and accountability | 113 | 19.1% |
| Data privacy and quality | 73 | 12.3% |
| Incident, change and decommissioning | 61 | 10.3% |
| Training, skills and resources | 55 | 9.3% |
| Third party, supply chain and procurement | 34 | 5.7% |
| Security and resilience | 22 | 3.7% |

Keyword matching is a transparent descriptive device, not a semantic model. An idea may appear without using the selected vocabulary, and a match does not prove depth. Still, the pattern is useful: test and monitoring work appears more often than policy/accountability vocabulary, and participation appears in more than one quarter of the action set.

## Action orientation

The most common normalized opening verb was **establish** (102 actions), followed by identify (40), document (23), verify (21), evaluate (20), utilize (19), define (17) and assess (13). Opening verbs are imperfect because bullet grammar varies. They nevertheless reinforce an operational reading: practitioners are asked to create, locate, record, test and maintain evidence.

## EVIDENCE-7: practical application

| Step | Manager action | Minimum artefact |
|---|---|---|
| Mandate | Define the use, owner and decision boundary | Approved use-case brief |
| Context | Map people, systems, data and affected groups | Context and impact map |
| Test | Specify performance, bias, privacy and security tests | Test protocol and results |
| Decide | Record thresholds, residual risk and authority | Decision memo |
| Monitor | Track drift, incidents, feedback and change | Monitoring register |
| Respond | Contain, correct, escalate or retire | Incident or change record |
| Review | Reassess evidence and improvement priorities | Periodic review pack |

For students, choose one low- or moderate-impact AI use case and build the seven records without confidential data. The quality test is traceability: a reviewer should be able to move from purpose and affected parties to tests, decision criteria, monitoring and an accountable response path.

## What managers should do next

1. Select one live or proposed AI use and name the accountable decision owner.
2. Define affected groups, data, model/service, human role and non-AI alternative.
3. Translate risk claims into testable criteria and evidence owners.
4. Set release thresholds before seeing results.
5. Create monitoring and incident triggers tied to actions and authority.
6. Review the evidence packet after material changes, not only on a fixed calendar.

MTF Institute's [AI Governance Manager: Lifecycle Controls, Evidence and Oversight](https://mtfinstitute.com/programs/ai-governance-manager-lifecycle-controls-evidence-oversight/#enroll) programme connects inventories, lifecycle controls, decision evidence, monitoring, incidents and executive oversight. Use EVIDENCE-7 as a capstone structure. The programme is professional education, not an academic degree, legal advice or a guarantee of employment or compliance.

## Limitations

The Playbook is a living public resource and may change after the capture date. Bullet granularity varies, so a count is not a measure of effort or importance. Nested bullets were treated as separate units when visibly presented that way. Keyword categories are non-exclusive and depend on the selected patterns. Opening verbs are syntactic signals, not full semantic classifications. The report studies guidance, not organizations, outcomes, adoption, legal duties or control effectiveness. NIST states that the Playbook offers suggested actions and is not a checklist or requirement to perform every action.

## Reproducibility and archival record

The searchable PDF and supporting workbook are archived at [Zenodo](https://zenodo.org/records/22642165) under DOI [10.5281/zenodo.22642165](https://doi.org/10.5281/zenodo.22642165). The workbook contains the complete 72-subcategory sampling frame, aggregate coding tables and protocol.

## Sources

- NIST AI Risk Management Framework Playbook: https://airc.nist.gov/airmf-resources/playbook/
- NIST AI Risk Management Framework 1.0: https://www.nist.gov/itl/ai-risk-management-framework



## Citation

When citing or summarizing this material, link to the canonical HTML page: https://mtfinstitute.com/insights/ai-risk-management-592-nist-ai-rmf-playbook-actions/