Enterprise AI governance is the structured oversight of how an organisation builds, buys and runs AI and machine-learning models so they stay lawful, safe and accountable. It involves five core components: a complete AI inventory, risk classification of each use case, independent model validation, ongoing performance and drift monitoring, and audit-ready attestation against frameworks such as the EU AI Act and NIST AI RMF.
| Framework | Origin | What it governs |
|---|---|---|
| EU AI Act | European Union | Risk-based legal obligations for AI providers and deployers, with tiers from prohibited to minimal risk |
| NIST AI RMF | United States (NIST) | Voluntary framework to govern, map, measure and manage AI risks across the lifecycle |
| ISO/IEC 42001 | ISO/IEC | Certifiable management-system standard for establishing and operating an AI management system |
| SR 11-7 | US Federal Reserve / OCC | Supervisory guidance on model risk management, validation and effective challenge for banks |
| PRA SS1/23 | Bank of England (PRA) | Model risk management principles for UK banks, covering identification, governance and validation |
Principal frameworks an enterprise AI governance programme is mapped against
Discover and record every AI and ML system, including embedded vendor features and generative tools, with owner, purpose, data and dependencies captured in a central register.
Assess each use case against EU AI Act risk tiers and internal risk appetite to set proportionate controls, prioritising high-risk and high-impact systems.
Apply independent model validation and effective challenge under SR 11-7 and PRA SS1/23, testing data, methodology, performance, bias and documentation.
Run post-market monitoring for drift, performance decay, bias and incidents, with thresholds, alerts and clear escalation routes to the AI risk committee.
Produce an audit pack with model cards, validation reports and control evidence mapped to each framework, ready for regulators, internal audit and the board.
“Done well, AI governance is an enabler, not a blocker: clear inventory, proportionate risk tiers and pre-agreed controls let teams move approved use cases into production with confidence rather than stalling on uncertainty.”
Enterprise AI governance is the structured oversight that keeps an organisation's AI lawful, safe and accountable across its lifecycle. It rests on five components: a complete AI inventory, risk classification, independent model validation, ongoing monitoring, and audit-ready attestation. Programmes are mapped to frameworks including the EU AI Act, NIST AI RMF, ISO/IEC 42001, SR 11-7 and PRA SS1/23. Moweb delivers this as a partner-led, fixed-fee engagement of 8 to 16 weeks, with an audit pack and a portable operating model on every engagement.
Approvals you can defend to regulators, customers and your own board
A governance practice that accelerates, rather than blocks, deployment
Reduced exposure to model-driven loss events
We open with a partner-led discovery - one week of interviews, data-room reads and stakeholder mapping. The output is a one-page thesis, not a deck.
Three to four weeks of solution design: architecture, control catalogue, build plan, success metrics, change posture. Reviewed with your model-risk, security and finance leads.
Joint Moweb-and-client pods deliver in 2-week iterations, with hard exit criteria for each release. Audit-pack evidence accumulates with every PR.
We co-run the system through the first three quarters. Hand-over is a transfer of practice, not just code.
We work alongside the major audit and advisory practices. Where they are less suited - building working policy, control catalogues and engineering-grade evidence - Moweb delivers. We do not provide tax, audit attestation or assurance opinions.
For a mid-sized enterprise: 12 to 18 weeks to inventory, classify, and ship a working governance operating model with the first cohort of high-risk systems remediated. Full estate compliance is a 9 to 14 month programme depending on starting maturity.
Translate ambition into an executable AI roadmap, scored against board-grade economics.
Production-grade copilots, agents and RAG systems engineered for accuracy, latency and cost.