White paper: AI Governance & Control Framework

AI has moved from a graveyard of failed projects to a jungle of systems deployed everywhere, often without oversight. The challenge is no longer whether AI works, but how to govern it responsibly while staying competitive.

Research shows that organizations investing in governance grow 2–3x faster on average (Berkman Klein Center). Effective oversight prevents costly rework, builds stakeholder trust, and accelerates go-to-market. 

As Pepijn van der Laan from Nemko puts its: 

“Regulation is coming. But more importantly, you can only successfully scale AI if you are in control. And corporate procurement departments are increasingly grilling AI suppliers on governance and controls. It is clearly time to get serious about AI Trust.” 

The three principles of effective AI Governance

 Our framework is built on three guiding principles that move governance beyond theory into practice: 

1. Practical implementation, not bureaucracy

Governance must be operational. The focus is on minimum viable controls that data science and compliance teams can actually use and scale as adoption grows.   

2. Risk-proportionate approaches   

Not all AI is equal. A predictive model for demand forecasting does not carry the same risks as a generative model that interacts with customers. Controls should match the actual risk level , avoiding both over-regulation of low-risk use cases and under-protection of high-risk applications.   

3. Lifecycle integration 

Governance cannot be bolted on after deployment. It must be part of the entire AI lifecycle from ideation and training to monitoring and retirement. Every stage brings specific risks, but also opportunities to prevent problems early, when they are cheapest to fix. 

From principles to practice 

Most organizations still struggle to make regulations and frameworks operational. The key is translating complex requirements into governance that teams can apply immediately with controls at both the organizational level and for individual AI systems.

Regulatory requirements for AI may seem abstract, but they translate directly into concrete technical and operational controls. 

• Organizational controls - like an AI registry, clear role assignment, and AI use policies provide structure.
• Technical controls - such as explainability, human oversight, audit trails, and performance monitoring ensure systems remain transparent, safe, and compliant in practice.
  
  Henning von Hauen, from Carve Consulting, who has been implementing these principles at organizations like Novo Nordisk, puts it this way:
  
  “Taking governance into technical controls with Deeploy, and integrating AI models with real-time risk management, compliance, and explainability is groundbreaking. It stimulates AI innovation while building trust.”