AI verification is the process of ensuring that an AI system behaves correctly, safely, and consistently according to defined rules and goals. As AI is deployed in healthcare, finance, law, and autonomous systems, verification becomes the foundation of trust.

Unlike traditional software, AI is probabilistic and learned from data rather than written as explicit rules. The same input can produce different outputs, and models contain billions of parameters, making exhaustive testing impossible. Human requirements such as “be truthful” or “avoid harm” are also difficult to formalize mathematically.

Because of this, full formal verification of general AI remains unsolved.

In practice, AI verification relies on:

• automated testing of model outputs
• reliability and risk scoring
• adversarial stress testing
• constraint and policy enforcement
• continuous monitoring in production

Verification is most critical in high-risk domains like medicine, law, and finance, where errors can cause real harm.

Without verification, hallucinations, bias, and unsafe behavior undermine trust and adoption. With it, AI systems can meet governance and compliance standards and operate more safely in real-world environments.

AI verification is the hardest trust problem in AI, and the most important. It is the bridge between powerful models and responsible deployment.