AI Governance as a Service

AI governance is one of the fastest-evolving functions in the enterprise, driven by the combined pressure of emerging regulation, increasing internal AI deployment, and the limitations of governance models that were designed for traditional software. The organizations keeping pace have moved beyond documentation exercises toward operational governance: risk-tiered intake, pre-approved control libraries, production monitoring, incident response, and reporting structures that produce audit-grade evidence as a byproduct of normal operations. Building that function from internal resources typically takes 12 to 24 months and requires capabilities that most enterprises are still recruiting for.

We compress that timeline by standing up and operating the governance function on behalf of the organization. The practice delivers the full program including policy framework, control library, model and agent registry, monitoring infrastructure, incident response capability, and board-level reporting. Engagements are designed as managed services for 12 to 18 months, with structured handoff to an internal owner once the client organization has built sufficient capacity, or on an indefinite operational basis where that structure better fits the client's posture.

Our work covers:

• AI policy framework calibrated to the organization's risk posture and regulatory exposure
• Risk classification system and use case intake process integrated with business workflows
• Control library mapped to risk tier and available to engineering teams as a pre-approved reference
• Model and agent registry with ownership, classification, and operational status
• Production monitoring including drift, hallucination, bias, and cost observability
• Incident response capability with tested playbooks and escalation paths
• Reporting infrastructure aligned to NIST AI RMF, ISO 42001, EU AI Act, and sector-specific frameworks