Practice · Data & AI

Make data & AI governance defensible.

Show clients where they stand on responsible AI and data governance, from ISO 42001, the NIST AI RMF and the EU AI Act through to GDPR accountability, graded on each standard's own scale.

UK regulatory coverage: 2 of these 10 frameworks are UK-specific, built for firms advising regulated UK clients.

12 frameworks

Data & AI frameworks you can run

Each runs an evidence-based maturity or readiness assessment, scored on the standard's own scale.

ISO/IEC 42001 AI Management System

Assess AI management-system readiness against ISO 42001 before certification.

Standard

NIST AI Risk Management Framework

Score AI risk practice across the NIST AI RMF Govern, Map, Measure and Manage functions.

Standard

EU AI Act Readiness

Classify AI systems by risk tier and assess obligations under the EU AI Act.

Regulation

AI Governance & Responsible AI Maturity

Measure responsible-AI maturity across governance, risk, transparency and oversight.

Model

Data Management Maturity (DAMA-DMBOK)

Score the data discipline against the DAMA-DMBOK knowledge areas: governance, quality, architecture and operations.

Model

UK AI Regulation (DSIT Principles & Assurance)

Check alignment to the UK's five AI-regulation principles and assurance expectations.

UK

UK GDPR & Data Protection / ICO Accountability

Assess UK GDPR accountability against the ICO's accountability framework.

UK

DCAM Data Management Capability

Benchmark capability against the EDM Council DCAM model, the data-governance standard favored in financial services.

Model

MLOps & Model Operations Maturity

Assess the maturity of model deployment, monitoring and lifecycle operations.

Model

Analytics & BI Maturity

Measure analytics and BI maturity from reporting to embedded decision intelligence.

Model

Data Strategy Assessment

Evaluate data maturity across governance, quality, architecture, analytics and culture.

Model

Data & AI Governance Assessment

Assess data governance, security, quality, sharing and AI ethics across the data lifecycle.

Model

How it works

From the client's documents to a board-ready deck.

1 · Evidence in

Upload the client's documents: policies, reports, data. An AI interviewer asks targeted follow-ups to fill anything missing.

2 · Scored on the standard

Every dimension is scored on the framework's own scale, with each score traceable to the evidence behind it, and gaps ranked by severity.

3 · Board-ready out

A board-ready slide deck and HTML report are generated automatically: executive summary, maturity landscape and a sequenced plan.

Evidence-grounded, not opinion

Defensible Data & AI scores.

Every score links back to the evidence it rests on, so the diagnosis holds up in the steering committee. See how evidence is collected and assessments are scored.

  • Scored against the standard's own bands, not a generic rubric
  • Gaps ranked by severity, ready to become the plan
  • Auto-generated slide deck and HTML report for the board
Evidence & assessments →
A scored data & ai maturity assessment with evidence-linked scores and ranked gaps

Run an assessment on real data.

We'll set up a framework live and score it from your client's own documents.