How do spam filters evolve over time?

Filters evolve through continuous learning from new spam techniques. Machine learning models retrain on current examples. Rule sets update as new patterns emerge. The arms race between spammers and filters drives ongoing adaptation.

Evolution trends: from keyword matching to behavioral analysis, from static rules to machine learning, and from sender-focused to engagement-focused evaluation. Each generation addresses limitations of previous approaches.

For senders, filter evolution means: practices working yesterday may not work tomorrow, keeping current with industry guidance matters, and building sustainable practices beats chasing loopholes.