The question we're answering
“How do we define allowed AI use, ownership, controls and evidence?” Most organizations don't lack AI enthusiasm. They lack a documented answer to what's approved, who signed off, and what happens when a new tool shows up next month.
Who this is for
Organizations rolling out AI tools or building AI into their products who need a real governance framework in place, not a one-page policy nobody follows.
What this covers
A governance engagement addresses each of the following directly, not as a checklist afterthought:
- Ownership: who approves new AI use, and who is accountable for it
- Data classification: what data classes are permitted as AI input, and which are not
- Model and provider boundaries: which AI providers and tools are approved, and under what terms
- Human approval: where a person must sign off before an AI-assisted decision takes effect
- Evidence: what gets logged and retained to prove the policy is actually followed
Outcomes & deliverables
- A documented AI use policy mapped to real ownership, not a generic template
- An approval workflow for new AI tools and use cases
- A data-classification standard specific to AI inputs and outputs
- An audit-ready evidence trail for board, customer or regulator review
How we approach it
Map current AI use across the organization, including ungoverned tools.
Build the policy, ownership model and approval workflow.
Stand up the evidence trail and the ongoing review cadence.
Supporting technology
Where governance tooling itself is part of the gap, we can point to Bonfy.AI, an affiliated technology, as one option for closing it, referenced only where it genuinely supports the outcome.
Regulatory readiness
Governance work here feeds directly into regulatory evidence, not just internal policy.
Industries & use cases
Proof
50 active enterprise clients, 100+ SMB clients, and 1,000+ assessments delivered per year: this isn't our first engagement like yours.
Expert reviewer
