Technical
Fine-Tuning
The process of training a pre-existing AI model on specialized data to improve its performance for a specific task.
Fine-tuning is the process of taking a pre-trained foundation model and further training it on a smaller, task-specific dataset to improve its performance for a particular use case. Organizations fine-tune models when they need:
- Domain-specific language understanding (legal, medical, financial terminology)
- Consistent output formatting or style
- Improved accuracy on specific task types
- Behavior that aligns with organizational policies
From a governance perspective, fine-tuning raises questions about data handling: the training data used for fine-tuning may contain sensitive information, and the resulting model may encode that information in its weights.
Why it matters
When a vendor says their AI is 'fine-tuned for your industry,' security teams should ask: fine-tuned on what data? Who has access to the fine-tuned model? Can the training data be extracted?