Data & AI frameworks
The Data & AI practice covers ten assessment frameworks spanning AI governance, AI regulation, and enterprise data management. Use them to score a client's AI management system, classify AI risk, check alignment to AI and data-protection law, or benchmark data and analytics capability. Each one runs against the client's own evidence and produces scored, cited findings, ranked gaps, and board-ready deliverables.
| Framework | Type | Use it when |
|---|---|---|
| ISO/IEC 42001 AI Management System | Standard | Assessing AI management-system readiness before certification |
| NIST AI Risk Management Framework | Standard | Scoring AI risk practice across Govern, Map, Measure, Manage |
| EU AI Act Readiness | Regulation | Classifying AI systems by risk tier and mapping obligations |
| AI Governance & Responsible AI Maturity | Model | Measuring responsible-AI maturity across governance and oversight |
| Data Management Maturity (DAMA-DMBOK) | Model | Scoring the data discipline against the DAMA-DMBOK knowledge areas |
| UK AI Regulation (DSIT Principles & Assurance) | Regulation · UK | Checking alignment to the UK's five AI-regulation principles |
| UK GDPR & Data Protection / ICO Accountability | Regulation · UK | Assessing UK GDPR accountability against the ICO framework |
| DCAM Data Management Capability | Model | Benchmarking data-management capability (financial services) |
| MLOps & Model Operations Maturity | Model | Assessing how reliably ML models run in production |
| Analytics & BI Maturity | Model | Measuring how well a client turns data into decisions |
How these assessments work
Every framework runs the same way: evidence in, scored on the standard's own scale, board-ready out. See Assessments Overview and Choosing a framework.