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Reference Framework · Regulatory-Aligned

Enterprise AI
Governance Framework

A comprehensive 6-pillar AI governance operating model aligned to the EU AI Act, ISO 42001 and NIST AI RMF. Designed for enterprise AI teams, risk functions and boards navigating AI governance in regulated and complex environments.

EU AI Act AlignedISO 42001 ReadyNIST AI RMF MappedBoard-Level ReadyRegulatory-Grade
6
Governance Pillars
4
Regulatory Frameworks Mapped
5
Maturity Levels
30+
Governance Controls

The Framework

Six Pillars of Enterprise AI Governance

Each pillar represents a distinct governance domain — from model inventory through to board reporting. Together they form a complete AI governance operating model that scales from startup to large enterprise.

PILLAR 01

AI Model Inventory & Classification

Every AI system in development or production must be inventoried, classified by risk tier and assigned ownership.

Complete register of all AI systems across the organisation
EU AI Act risk classification: Unacceptable / High / Limited / Minimal
ISO 42001 alignment for AI management system certification pathway
Clear ownership: model owner, technical owner, risk owner, business sponsor
Mandatory review trigger for new AI systems before development begins

EU AI Act Art. 49–51 · ISO 42001 §6 · NIST AI RMF GOVERN 1.1

PILLAR 02

Accountability & Governance Structure

Governance requires clear accountability structures — not just policy documents. Every AI system needs named owners responsible for performance, ethics and compliance.

AI Governance Committee with board-level sponsorship
RACI matrix for all AI systems: development, deployment and oversight
Named model owner accountable for production performance and bias
Independent ethics review for high-risk AI systems
Escalation path from AI team to board for significant AI risks

EU AI Act Art. 16–24 · FCA PS22/3 · NIST AI RMF GOVERN 2.1

PILLAR 03

Model Documentation Standards

Undocumented AI is ungoverned AI. Every production AI system requires standardised documentation covering purpose, training, performance, limitations and risks.

Model Card for every production AI system (performance, training data, limitations)
System Card for customer-facing AI products
Data Sheet documenting training dataset composition, collection and quality
Intended vs prohibited use specification
Known failure modes and edge cases documented
Version history and change log for all model updates

EU AI Act Annex IV · Model Cards (Mitchell et al.) · FCA SS1/23

PILLAR 04

Bias, Fairness & Ethics Controls

AI systems must not perpetuate or amplify discrimination. Fairness evaluation must be built into the development lifecycle — not added as an afterthought.

Mandatory bias evaluation across protected characteristics before deployment
Disparate impact testing: statistical parity, equalised odds, predictive parity
Demographic performance disaggregation in model cards
Prohibited use cases defined and technically enforced
Ethics review gate for high-risk AI systems before deployment approval
Ongoing fairness monitoring in production with drift alerting

EU AI Act Art. 10 · GDPR Art. 22 · IEEE 7000 · NIST AI RMF MANAGE 1.3

PILLAR 05

Ongoing Monitoring & Oversight

Governance is continuous — not a one-time deployment gate. Production AI systems require ongoing performance monitoring, drift detection and structured review cycles.

Performance SLOs defined for every production AI system
Data drift and concept drift monitoring with automated alerting
Quarterly model performance reviews with model owner and risk function
Trigger-based retraining process with governance approval
AI incident classification, logging and response protocol
Annual full governance framework review

EU AI Act Art. 72 · SR 11-7 (FRB) · NIST AI RMF MEASURE 2.5

PILLAR 06

Board & Executive Reporting

AI risk must be visible at board level. Standardised reporting allows executives to understand the organisation's AI risk posture without requiring technical expertise.

Quarterly AI risk report to board/risk committee
AI portfolio dashboard: model count, risk tiers, compliance status, incidents
Key risk indicators for AI: drift events, bias flags, incidents, coverage gaps
Material AI risk escalation threshold defined and agreed at board level
External audit readiness: documentation, evidence, governance trail

EU AI Act Art. 9 · FCA DP5/22 · Basel III AI Ops Risk · NIST AI RMF GOVERN 6.1

Maturity Assessment

AI Governance Maturity Model

Where does your organisation sit? Use this model to assess your current governance maturity and define your target state.

0–2

Level 1

Ad Hoc

No formal AI governance. Models deployed without documentation, ownership or oversight.

3–4

Level 2

Aware

Basic model inventory exists. Some documentation. No formal governance structure or ongoing oversight.

5–6

Level 3

Defined

Governance policy in place. Model cards for key systems. Named ownership. Quarterly reviews starting.

7–8

Level 4

Managed

Full inventory, documentation and bias evaluation. Active governance committee. Board reporting in place.

9–10

Level 5

Optimised

Continuous monitoring, automated governance triggers, ISO 42001 alignment, proactive regulatory engagement.

Regulatory Alignment

Key Regulatory Frameworks

This governance framework maps directly to the major AI regulatory and standards frameworks. Understanding each and their requirements is essential for enterprise AI risk management.

EU AI Act

High-risk AI systems deployed in EU

In force Aug 2024
Risk classificationConformity assessmentHuman oversightTechnical documentationTransparency obligations

ISO/IEC 42001

AI Management System certification

Published Dec 2023
AI policyRisk assessmentControlsPerformance evaluationContinuous improvement

NIST AI RMF

US federal agencies + enterprise

Published Jan 2023
GOVERNMAPMEASUREMANAGE functions

FCA AI/ML

UK financial services

SS1/23 active
Model risk managementExplainabilityBias testingSenior accountability

Assess Your AI Governance Maturity

MTC's AI governance assessment maps your current state across all six pillars, identifies regulatory gaps and delivers a prioritised remediation roadmap.