Most AI governance frameworks are written by legal teams trying to prevent liability rather than by operators trying to enable safe deployment. The result is a document nobody reads that protects nobody. This is the version that actually works.
A functional AI governance framework has four components: a data classification policy that tells teams what can go where, an output review standard that scales with risk level, a vendor management process that takes less than a day per vendor, and an incident response protocol that people can actually follow under pressure.
The most important AI governance decision you will make is: which data categories can go into which AI systems. Get this wrong in either direction and you have a problem. Too restrictive, and teams route around your governance framework using personal accounts. Too permissive, and you have a real data exposure risk.
A practical three-tier classification works for most B2B companies. Tier 1 is public information: anything already on your website, general market knowledge, generic business content. This can go into any AI system. Tier 2 is internal information: employee names and roles, internal project names, general business strategy, non-sensitive financial summaries. This can go into AI systems with standard data processing agreements. Tier 3 is confidential information: individual customer data, specific contract terms, personal employee information, proprietary technical specifications. This requires explicit authorization and specific vendor DPA review before going into any AI system.
The key governance decision is not how to handle Tier 3 -- the answer is always careful review. The key decision is where to draw the line between Tier 2 and Tier 3 for your specific business. That boundary should be set by legal and operations together, reviewed annually, and communicated to all teams before any AI deployment.
Not all AI outputs carry the same risk. A marketing headline that contains an error is annoying. A legal clause that contains an error is a liability. Your output review standards should reflect this difference.
Low-risk outputs (internal documents, first-draft content for human editing, data summaries for internal use) should get a light review: one person reads for obvious errors, no formal approval required. Medium-risk outputs (client-facing documents, external communications, financial projections) should get a standard review: the document owner reviews against a checklist, one additional reviewer for anything above a dollar threshold. High-risk outputs (legal documents, regulatory filings, formal advice) should not be produced by AI without explicit policy authorization, and when they are, they require the same review as a human-drafted version.
The practical implication is that your teams need to know which category their outputs fall into before they start the workflow. Build the risk category into the workflow documentation, not as an afterthought.
The review standard for AI-generated output should match the review standard you would apply to a new employee doing the same task. A junior employee would not send a client contract without a senior review. Neither should your AI workflow.
Enterprise AI vendor management processes are notoriously slow. Legal wants a DPA review. Security wants a penetration test report. IT wants an infrastructure assessment. Meanwhile, the team that needs the tool is using a personal account to route around the process.
For AI tools, a streamlined vendor process works: a standard questionnaire (data residency, training opt-out, SOC 2 status, DPA availability), a 5-business-day SLA for legal DPA review, and a standard approval matrix (tools under a cost threshold get approved by the AI lead, above the threshold go to a Steering Group). The total elapsed time for a standard AI tool approval should be under 10 business days.
Anthropic specifically: Claude via the API includes a data processing agreement and a training opt-out option. Data sent through the API is not used for model training by default. That removes the biggest governance concern for most B2B use cases and means Claude can typically pass a standard vendor review in a single working day.
AI incidents will happen. A workflow will produce an incorrect output that gets sent to a client. A model will hallucinate a fact in a report. An automated process will make a decision that should have had human review. Your governance framework needs a protocol for these events.
The protocol has four steps: contain (stop the affected workflow, identify all outputs that may be affected), assess (determine the scope of impact and whether any external party received incorrect information), remediate (correct the record, notify affected parties where required), and improve (update the workflow documentation to prevent recurrence). The protocol should be written down, assigned to specific roles, and practiced before an incident occurs.
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