Enterprise AI Governance Framework
Advisory and framework design for enterprise AI controls, decision rights, and assurance models in regulated environments.
A body of advisory and framework design work spanning UK government clients and regulated industries — focused on governance systems that support speed and accountability.
The Problem
Most AI governance in enterprise today was designed for supervised machine learning: model risk management, bias auditing, data quality checks. Those controls don't break down slowly as you introduce agentic AI — they break down immediately.
The Approach
The work spans governance framework design, AI security posture assessments, and operating model advisory for organisations introducing autonomous AI capabilities into existing risk and compliance structures.
The common thread: governance that accelerates rather than blocks.
Practical governance means translating principles into architecture standards, review checkpoints, and telemetry requirements that teams can implement without interpretation overhead. It means defining a small set of required design artefacts per initiative, attaching risk tiers to concrete technical controls, making approvals time-boxed and evidence-based, and treating post-deployment monitoring as part of governance rather than an afterthought.
Specific Areas
AI governance frameworks for GDS and departmental AI programmes under AI Safety Institute scrutiny.
Security architecture for AI systems, including DPIA-style assessments.
Compliance posture design for emerging regulatory frameworks — EU AI Act obligations, MHRA, ICO requirements.
For organisations where governance, architecture, and delivery are currently designed as separate programmes. Decision rights frameworks that define accountability when autonomous behaviour is introduced.
Details available on request.