How do AI filters differ from traditional ones?

Traditional filters follow explicit rules written by humans. Each rule tests for a specific pattern and adds points to a spam score. The rules are transparent and explainable.

AI filters learn patterns from data rather than following predefined rules. Machine learning models discover correlations that predict spam without being explicitly programmed for each pattern.

AI filters adapt faster to new threats because they can learn from examples rather than waiting for humans to write new rules. They detect subtle patterns humans might miss.

AI filters are less explainable. When a message is filtered, it may be difficult to identify exactly why. The model knows, but it cannot articulate its reasoning in human terms.

Modern filtering systems typically combine both approaches: AI for pattern detection plus traditional rules for known issues and policy enforcement.

AI filters develop intuition from experience rather than following instructions. They are powerful but sometimes inscrutable.