Paloren compiles AI governance statistics and public frameworks from OECD and NIST.
Paloren compiles AI governance statistics and public frameworks from OECD.AI and NIST. See how policy coverage and risk-management resources help teams build accountable controls.
For teams building AI governance.
AI governance evidence includes OECD.AI, which covers more than 80 jurisdictions and organisations, and NIST, which publishes the voluntary AI Risk Management Framework and playbook. Companies still need workflow-specific controls.
OECD.AI covers more than 80 jurisdictions and organisations.
Inside the detail
OECD.AI describes its policy navigator as a living repository from more than 80 jurisdictions and organisations. It is updated by official contact points and OECD.AI experts.
This is a useful source for understanding public policy coverage, but it does not tell a company what internal controls are sufficient for its workflow.
Keep these in view
More than 80 jurisdictions and organisations
Living public repository
Policy initiatives database
The AI Risk Management Framework and playbook.
Inside the detail
NIST provides the AI Risk Management Framework, a voluntary framework to help organisations manage risks to individuals, organisations and society associated with AI. It includes a playbook and related resources.
NIST has also released a generative AI profile. These resources support design decisions such as governance, measurement and risk handling.
Keep these in view
AI Risk Management Framework
Public playbook
Generative AI profile
| Source | What it provides | Link |
|---|---|---|
| OECD.AI Policy Navigator | Public AI policies and initiatives from more than 80 jurisdictions and organisations | https://oecd.ai/en/dashboards |
| NIST AI RMF | Voluntary risk-management framework, playbook and generative AI profile | https://www.nist.gov/itl/ai-risk-management-framework |
Public frameworks support design; internal owners still matter.
Source: OECD.AI and NIST public pages.
To compare scope, not to replace controls.
Inside the detail
Policy and framework statistics help teams understand external context. They do not replace workflow-specific governance. Paloren recommends mapping AI use cases, data access, approval routes, quality checks, audit evidence and named owners. This creates controls people can follow in the actual workflow.
Keep these in view
Use-case inventory
Access and approvals
Audit and owners
Paloren turns OECD.AI policy coverage and the NIST AI Risk Management Framework into workflow-specific controls. Statistics describe the external landscape; S4 converts that context into named owners, approval routes and audit evidence.
| Stage | What changes |
|---|---|
| Signal | Record which AI use cases exist, which OECD.AI jurisdictions touch the relevant policy areas, and which NIST risk functions already have owners. |
| Synthesis | Map each use case against NIST governance, measurement and risk-management functions, drafting an approval matrix and data-access rules per workflow. |
| System | Connect the mapped use cases to approval routes, quality checks and audit evidence, testing failure paths such as missing review or unowned data access. |
| Scale | Assign named owners to track policy changes in the OECD.AI navigator and generative AI profile, extending controls one use case at a time. |
A 60-person company reviews AI-assisted document drafting. Signal logs eight use cases and finds three policy areas covered by OECD.AI entries from multiple jurisdictions.
Synthesis maps each case to NIST risk-management functions and sets approval thresholds. System tests twelve historical outputs against quality checks and evidence trails.
Scale assigns a compliance lead to monitor navigator updates quarterly and audits whether every use case still has a named owner and current approval route.
Written teaching example with hypothetical inputs, not a client result.
Operating metric: Percentage of AI use cases with a named owner and audit evidence.
What you take forward
Build AI governance
Tell Paloren the systems, data and approval owners.
List systems, data and decisions.
Identify access and failure points.
Define approvals and evidence.
Name accountability and review.
| Source | What it provides | Link |
|---|---|---|
| OECD.AI Policy Navigator | Public AI policies and initiatives from more than 80 jurisdictions and organisations | https://oecd.ai/en/dashboards |
| NIST AI RMF | Voluntary risk-management framework, playbook and generative AI profile | https://www.nist.gov/itl/ai-risk-management-framework |
Public frameworks support design; internal owners still matter.
OECD.AI and NIST public pages.
Use-case inventory
Risk map
Control matrix
Approval routes
Audit plan
Owner register
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | Record which AI use cases exist, which OECD.AI jurisdictions touch the relevant policy areas, and which NIST risk functions already have owners. |
| 02 | Synthesis | Map each use case against NIST governance, measurement and risk-management functions, drafting an approval matrix and data-access rules per workflow. |
| 03 | System | Connect the mapped use cases to approval routes, quality checks and audit evidence, testing failure paths such as missing review or unowned data access. |
| 04 | Scale | Assign named owners to track policy changes in the OECD.AI navigator and generative AI profile, extending controls one use case at a time. |
OECD.AI describes its policy navigator as covering more than 80 jurisdictions and organisations.
A voluntary framework to help organisations manage AI-related risks, with a public playbook.
No. It supports design. Legal and regulatory interpretation remains with qualified advisers.
Use-case inventory, access rules, approval routes, quality checks, audit evidence and owners.
Which AI use cases need controls?
Build AI governance
