Enterprise AI Solutions

AI Governance Framework

Before AI use spreads beyond a pilot team, or after it already has and nobody can say who is using which capability on what data.

A governance framework defines who may use which AI capabilities, on what data, with what oversight, and with what record. The framework only matters if it is enforced by infrastructure: a policy stating that outputs must be reviewed changes nothing if the tooling does not make review mandatory and traceable.

Frameworks that survive contact with real workloads are proportionate. Requiring human review of every decision creates a bottleneck teams route around, and a bypassed control gives worse assurance than a lighter one people actually follow.

What the work includes

  • Decision risk tiering, with criteria covering reversibility, blast radius, and external visibility
  • Role-based access rules enforced in the request pipeline rather than the interface
  • Audit requirements covering who requested what, which model ran, and what followed
  • Incident response paths for when an AI system behaves unexpectedly
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// Common Questions

Questions
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Common questions about AI Governance Framework.

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Who may use which AI capabilities, on what data, with what oversight, and what record is kept. It covers approval paths for new use cases, handling of sensitive data, and the audit trail needed when someone asks what a system did and why.

By implementing the rules in infrastructure — access control, output filtering, logging — so the boundaries hold whether or not anyone remembers the policy. Documented governance that is not enforced changes nothing except what an organization believes about itself.

Before use spreads beyond a pilot team. Retrofitting governance means discovering what people have already been doing with which data, which is a harder conversation than setting the boundary first. If AI use has already spread, the first task is establishing what is actually running.

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