01 / EMPOWERING INDIVIDUALS
By Principle.
Best practices.Applied principles.Personal and professional commitments.
FRAMEWORK / MATURITY MODEL
Machine Learning Maturity Model
Make safe and aligned ML repeatable — a practical path from isolated good intentions to adaptive organisational capability.
THE FOUR-PHASE AI SAFETY STRATEGY
From individual practice to national regulation.
02 / EMPOWERING ORGANISATIONS
By Process.
Practical industry frameworks.Applied guides.Principles translated into implementation.
03 / EMPOWERING INDUSTRIES
By Standards.
Technical and industry standards.Standards bodies engagement.Cross-industry initiatives.
04 / EMPOWERING NATIONS
By Regulation.
Policy and regulatory work.Public frameworks and requirements.International governance.
DELIVERY CHECKLIST
Evidence suppliers should provide
| Assessment area | Minimum supplier evidence |
|---|---|
| Practical benchmarks | Use-case measures beyond accuracy, with processing time, training resources and operating requirements |
| Explainability by justification | A proportionate account of system outputs, supported by audit trails and domain expertise |
| Data and model assessment | Processes for identifying, documenting and mitigating unwanted bias under production conditions |
| Reproducible operations | Model versioning, rollback, diagnosis, redeployment and separated development and serving workflows |
| Privacy-enforcing infrastructure | Personal-data controls, consent handling, privacy documentation and re-identification safeguards |
| Operational process design | Fail-safe steps, human review and escalation based on the impact of incorrect outputs |
| Change-management capabilities | Rollout, workforce impact, training and handover plans for affected stakeholders |
| Security risk processes | Access control, data protection, secured infrastructure and operator education |
CAPABILITIES
The system around the model
BenchmarkingMeasures reflect the use case, error costs, time and resource requirements.
JustificationOperators can inspect an appropriate account of how outputs were produced.
AssessmentData and model behaviour are evaluated for unwanted bias and production change.
ReproducibilityModels and operations can be versioned, repeated, diagnosed and rolled back.
PrivacyPersonal data is separated, protected and governed according to consent.
OperationsHuman review and fail-safe steps are designed around the impact of errors.
Change managementRollout, training and handover account for the people affected by automation.
SecurityModels, data, infrastructure and operating processes are protected together.
01 — HOW THE FRAMEWORKS CONNECT
How the frameworks connect
The maturity model is what AI-RFX scores against: each procurement criterion asks where the organisation sits on the model's capability levels. Both inherit their targets from the Nine Principles for AI Alignment & Safety.
Agent identity, mandate boundaries and action observability are being drafted as part of the Agentic Maturity Model.