AI Glossary
Model Integrity
The assurance that an AI model remains accurate, trustworthy, and free from unauthorized modification.
Model Integrity
Overview
Organizations rely on AI models to produce consistent and reliable outputs.
For this to happen, the models themselves must remain trustworthy.
This concept is known as model integrity.
Model integrity refers to protecting AI models from unauthorized changes, corruption, tampering, or unexpected modifications.
A helpful way to think about model integrity is maintaining an official document.
If someone alters the document without authorization, confidence in its contents decreases.
The same principle applies to AI systems.
Organizations need confidence that deployed models behave as expected and have not been modified improperly.
Maintaining model integrity may involve version control, monitoring, validation processes, access controls, and governance practices.
As AI becomes more integrated into critical operations, model integrity is becoming increasingly important.
Why It Matters
Model integrity helps ensure AI systems remain reliable, trustworthy, and consistent.
Real-World Example
A company may verify model versions before deployment to ensure unauthorized changes have not occurred.